def main():
rospy.init_node('tf_test')
rospy.loginfo("Node for testing actionlib server")
#from_link = '/head_color_camera_l_link'
#to_link = '/base_link'
from_link = '/base_link'
to_link = '/map'
tfl = TransformListener()
rospy.sleep(5)
t = rospy.Time(0)
mpose = PoseStamped()
mpose.pose.position.x = 1
mpose.pose.position.y = 0
mpose.pose.position.z = 0
mpose.pose.orientation.x = 0
mpose.pose.orientation.y = 0
mpose.pose.orientation.z = 0
mpose.pose.orientation.w = 0
mpose.header.frame_id = from_link
mpose.header.stamp = rospy.Time.now()
rospy.sleep(5)
mpose_transf = None
rospy.loginfo('Waiting for transform for some time...')
tfl.waitForTransform(to_link,from_link,t,rospy.Duration(5))
if tfl.canTransform(to_link,from_link,t):
mpose_transf = tfl.transformPose(to_link,mpose)
print mpose_transf
else:
rospy.logerr('Transformation is not possible!')
sys.exit(0)
class Leader():
def __init__(self, goals):
rospy.init_node('leader', anonymous=True)
self.worldFrame = rospy.get_param("~worldFrame", "/world")
self.frame = rospy.get_param("~frame")
self.leaderFrame = rospy.get_param("~leaderFrame", "/leader")
self.pubGoal = rospy.Publisher('goal', PoseStamped, queue_size=1)
# self.leaderAdvertise=rospy.Publisher('leaderPosition',PoseStamped,queue_size=1)
self.listener = TransformListener()
rospy.Subscriber("cmd_vel", Twist, self.cmdVelCallback)
self.goals = goals
self.takeoffFlag = 0
self.goalIndex = 0
rospy.loginfo("demo start!!!!!!!")
def cmdVelCallback(self, data):
if data.linear.z != 0.0 and self.takeoffFlag == 0:
self.takeoffFlag = 1
rospy.sleep(10)
self.takeoffFlag = 2
def run(self):
self.listener.waitForTransform(self.worldFrame, self.frame,
rospy.Time(), rospy.Duration(5.0))
goal = PoseStamped()
goal.header.seq = 0
goal.header.frame_id = self.worldFrame
while not rospy.is_shutdown():
self.calc_goal(goal, self.goalIndex)
self.pubGoal.publish(goal)
# self.leaderAdvertise.publish(goal)
t = self.listener.getLatestCommonTime(self.worldFrame, self.frame)
if self.listener.canTransform(self.worldFrame, self.frame, t):
position, quaternion = self.listener.lookupTransform(
self.worldFrame, self.frame, t)
rpy = tf.transformations.euler_from_quaternion(quaternion)
if self.takeoffFlag == 1:
self.goalIndex = 0
elif self.takeoffFlag == 2 and self.goalIndex < len(
self.goals) - 1:
rospy.sleep(self.goals[self.goalIndex][4] * 2)
rospy.loginfo(self.goalIndex)
self.goalIndex += 1
def calc_goal(self, goal, index):
goal.header.seq += 1
goal.header.stamp = rospy.Time.now()
goal.pose.position.x = self.goals[index][0]
goal.pose.position.y = self.goals[index][1]
goal.pose.position.z = self.goals[index][2]
quaternion = tf.transformations.quaternion_from_euler(
0, 0, self.goals[index][3])
goal.pose.orientation.x = quaternion[0]
goal.pose.orientation.y = quaternion[1]
goal.pose.orientation.z = quaternion[2]
goal.pose.orientation.w = quaternion[3]
class Follower():
def __init__(self, leaderFrame, radius=0.5, phase=0, pointNum=2000):
rospy.init_node('follower', anonymous=True)
self.worldFrame = rospy.get_param("~worldFrame", "/world")
self.frame = rospy.get_param("~frame")
self.pubGoal = rospy.Publisher('goal', PoseStamped, queue_size=1)
# rospy.Subscriber("/"+leaderName+'/leaderPosition',PoseStamped,self.followerSubCB)
self.listener = TransformListener()
self.goal = PoseStamped()
self.leaderFrame = leaderFrame
self.radius = radius
self.phase = 0
self.pointNum = 2000
self.goalIndex = 0
rospy.loginfo("demo start!!!!!!!")
def run(self):
self.listener.waitForTransform(self.worldFrame, self.frame,
rospy.Time(), rospy.Duration(5.0))
self.goal = PoseStamped()
self.goal.header.seq = 0
self.goal.header.frame_id = self.worldFrame
self.goal.pose.orientation.w = 1
while not rospy.is_shutdown():
self.pubGoal.publish(self.goal)
# rospy.loginfo(self.goal)
# rospy.loginfo(self.worldFrame)
# rospy.loginfo(self.leaderFrame)
# rospy.loginfo(self.frame)
t = self.listener.getLatestCommonTime(self.worldFrame,
self.leaderFrame)
if self.listener.canTransform(self.worldFrame, self.leaderFrame,
t):
position, quaternion = self.listener.lookupTransform(
self.worldFrame, self.leaderFrame, t)
# rospy.loginfo(position)
# rospy.loginfo(quaternion)
self.followerGoalGenerate(position, quaternion)
rospy.sleep(0.02)
def followerGoalGenerate(self, position, quaternion):
# rospy.loginfo("info received!")
angle = self.goalIndex / self.pointNum * 2 * math.pi + self.phase
self.goal.header.seq += 1
self.goal.header.stamp = rospy.Time.now()
self.goal.pose.position.x = position[0] + self.radius * math.cos(angle)
self.goal.pose.position.y = position[1] + self.radius * math.sin(angle)
# self.goal.pose.position.z=position.z
self.goal.pose.position.z = 0.8
self.goal.pose.orientation.w = quaternion[3]
self.goal.pose.orientation.x = quaternion[0]
self.goal.pose.orientation.y = quaternion[1]
self.goal.pose.orientation.z = quaternion[2]
self.goalIndex = self.goalIndex + 1
class Normal():
def __init__(self):
rospy.init_node('follower', anonymous=True)
self.worldFrame = rospy.get_param("~worldFrame", "/world")
self.frame = rospy.get_param("~frame")
self.pubGoal = rospy.Publisher('goal', PoseStamped, queue_size=1)
# rospy.Subscriber("/"+leaderName+'/leaderPosition',PoseStamped,self.followerSubCB)
self.listener = TransformListener()
self.goal=PoseStamped()
# 第一件事情，跟随这个目标，这个目标的格式应该是一个frame
self.leaderFrame=rospy.get_param("~leaderFrame","")
self.offsetX=rospy.get_param("~offsetX","0")
self.offsetY=rospy.get_param("~offsetY","0")
# self.offsetZ=rospy.get_param("~offsetZ","0")
# 第二件事情，广播自身的位置吧
# self.pubAttitude=rospy.Publisher('pose',)
# 第三件事情，侦听manager节点的状态信息
self.takeoffFlag = 0
self.goalIndex = 0
rospy.loginfo("demo start!!!!!!!")
def run(self):
self.listener.waitForTransform(self.worldFrame, self.frame, rospy.Time(), rospy.Duration(5.0))
self.goal = PoseStamped()
self.goal.header.seq = 0
self.goal.header.frame_id = self.worldFrame
self.goal.pose.orientation.w=1
while not rospy.is_shutdown():
self.pubGoal.publish(self.goal)
# rospy.loginfo(self.goal)
# rospy.loginfo(self.worldFrame)
# rospy.loginfo(self.leaderFrame)
# rospy.loginfo(self.frame)
t = self.listener.getLatestCommonTime(self.worldFrame, self.leaderFrame)
if self.listener.canTransform(self.worldFrame, self.leaderFrame, t):
position, quaternion = self.listener.lookupTransform(self.worldFrame, self.leaderFrame, t)
# rospy.loginfo(position)
# rospy.loginfo(quaternion)
self.followerGoalGenerate(position,quaternion)
rospy.sleep(0.02)
def followerGoalGenerate(self,position,quaternion):
# rospy.loginfo("info received!")
self.goal.header.seq += 1
self.goal.header.stamp = rospy.Time.now()
self.goal.pose.position.x=position[0]+float(self.offsetX)
self.goal.pose.position.y=position[1]+float(self.offsetY)
# self.goal.pose.position.z=position.z
self.goal.pose.position.z=0.8
self.goal.pose.orientation.w=quaternion[3]
self.goal.pose.orientation.x=quaternion[0]
self.goal.pose.orientation.y=quaternion[1]
self.goal.pose.orientation.z=quaternion[2]
def main():
rospy.init_node('tf_test')
rospy.loginfo("Node for testing actionlib server")
#from_link = '/head_color_camera_l_link'
#to_link = '/base_link'
from_link = '/base_link'
to_link = '/map'
tfl = TransformListener()
rospy.sleep(5)
t = rospy.Time(0)
mpose = PoseStamped()
mpose.pose.position.x = 1
mpose.pose.position.y = 0
mpose.pose.position.z = 0
mpose.pose.orientation.x = 0
mpose.pose.orientation.y = 0
mpose.pose.orientation.z = 0
mpose.pose.orientation.w = 0
mpose.header.frame_id = from_link
mpose.header.stamp = rospy.Time.now()
rospy.sleep(5)
mpose_transf = None
rospy.loginfo('Waiting for transform for some time...')
tfl.waitForTransform(to_link, from_link, t, rospy.Duration(5))
if tfl.canTransform(to_link, from_link, t):
mpose_transf = tfl.transformPose(to_link, mpose)
print mpose_transf
else:
rospy.logerr('Transformation is not possible!')
sys.exit(0)
class Demo():
def __init__(self, goals):
rospy.init_node('demo', anonymous=True)
self.worldFrame = rospy.get_param("~worldFrame", "/world")
self.frame = rospy.get_param("~frame")
self.pubGoal = rospy.Publisher(
'goal', PoseStamped,
queue_size=1) #publish to topic goal with msg type PoseStamped
self.listener = TransformListener(
) #tflisterner is a method with functions relating to transforms
self.goals = goals #Assign nX5 matrix containing target waypoints
self.goalIndex = 0 # start with the first row of entries (first waypoint)
def run(self):
self.listener.waitForTransform(
self.worldFrame, self.frame, rospy.Time(), rospy.Duration(5.0)
) #Find the transform from world frame to body frame, returns bool on if it can find a transform
goal = PoseStamped()
goal.header.seq = 0
goal.header.frame_id = self.worldFrame
while not rospy.is_shutdown():
goal.header.seq += 1
goal.header.stamp = rospy.Time.now()
goal.pose.position.x = self.goals[self.goalIndex][0]
goal.pose.position.y = self.goals[self.goalIndex][1]
goal.pose.position.z = self.goals[self.goalIndex][2]
quaternion = tf.transformations.quaternion_from_euler(
0, 0, self.goals[self.goalIndex][3])
goal.pose.orientation.x = quaternion[0]
goal.pose.orientation.y = quaternion[1]
goal.pose.orientation.z = quaternion[2]
goal.pose.orientation.w = quaternion[3]
self.pubGoal.publish(goal)
t = self.listener.getLatestCommonTime(self.worldFrame, self.frame)
if self.listener.canTransform(self.worldFrame, self.frame, t):
position, quaternion = self.listener.lookupTransform(
self.worldFrame, self.frame, t)
rpy = tf.transformations.euler_from_quaternion(quaternion)
#If within position error bound, sleep and then move to next waypoint
if math.fabs(position[0] - self.goals[self.goalIndex][0]) < 0.2 \
and math.fabs(position[1] - self.goals[self.goalIndex][1]) < 0.2 \
and math.fabs(position[2] - self.goals[self.goalIndex][2]) < 0.2 \
and math.fabs(rpy[2] - self.goals[self.goalIndex][3]) < math.radians(10) \
and self.goalIndex < len(self.goals) - 1:
rospy.sleep(self.goals[self.goalIndex][4])
self.goalIndex += 1
class Plotter:
def __init__(self):
self.fig = pp.figure()
self.lin_ax = AxisFig(self.fig, 211)
self.ang_ax = AxisFig(self.fig, 212, False)
self.fig.tight_layout()
self.canvas = FigureCanvas(self.fig)
self.tl = TransformListener()
self.des_sub = rospy.Subscriber('/desired_pose', Float32MultiArray,
self.des_pose_cb)
self.des_pose = []
def des_pose_cb(self, msg):
self.des_pose = msg.data
def loop(self):
while not rospy.is_shutdown():
# update TF
if self.tl.canTransform('base_link', 'tool0', rospy.Time(0)):
tr = self.tl.lookupTransform('base_link', 'tool0',
rospy.Time(0))
d = False
# angle-axis from quaternion
s2 = pl.sqrt(tr[1][0]**2 + tr[1][1]**2 + tr[1][2]**2)
tu = [0, 0, 0]
if s2 > 1e-6:
t = pl.arctan2(s2, tr[1][3]) * 2
if t > pl.pi:
t -= 2 * pl.pi
tu = [t * tr[1][i] / s2 for i in range(3)]
if len(self.des_pose):
d = self.lin_ax.update(tr[0], self.des_pose[:3])
self.ang_ax.update(tu, self.des_pose[3:])
else:
d = self.lin_ax.update(tr[0])
self.ang_ax.update(tu)
if d:
self.canvas.draw()
rospy.sleep(0.1)
class Demo():
def __init__(self, goals):
rospy.init_node('demo', anonymous=True)
self.worldFrame = rospy.get_param("~worldFrame", "/world")
self.frame = rospy.get_param("~frame")
self.pubGoal = rospy.Publisher('goal', PoseStamped, queue_size=1)
self.listener = TransformListener()
self.goals = goals
self.goalIndex = 0
def run(self):
self.listener.waitForTransform(self.worldFrame, self.frame,
rospy.Time(), rospy.Duration(5.0))
goal = PoseStamped()
goal.header.seq = 0
goal.header.frame_id = self.worldFrame
while not rospy.is_shutdown():
goal.header.seq += 1
goal.header.stamp = rospy.Time.now()
goal.pose.position.x = self.goals[self.goalIndex][0]
goal.pose.position.y = self.goals[self.goalIndex][1]
goal.pose.position.z = self.goals[self.goalIndex][2]
quaternion = tf.transformations.quaternion_from_euler(
0, 0, self.goals[self.goalIndex][3])
goal.pose.orientation.x = quaternion[0]
goal.pose.orientation.y = quaternion[1]
goal.pose.orientation.z = quaternion[2]
goal.pose.orientation.w = quaternion[3]
self.pubGoal.publish(goal)
t = self.listener.getLatestCommonTime(self.worldFrame, self.frame)
if self.listener.canTransform(self.worldFrame, self.frame, t):
position, quaternion = self.listener.lookupTransform(
self.worldFrame, self.frame, t)
rpy = tf.transformations.euler_from_quaternion(quaternion)
if math.fabs(position[0] - self.goals[self.goalIndex][0]) < 0.1 \
and math.fabs(position[1] - self.goals[self.goalIndex][1]) < 0.1 \
and math.fabs(position[2] - self.goals[self.goalIndex][2]) < 0.1 \
and math.fabs(rpy[2] - self.goals[self.goalIndex][3]) < math.radians(8):
if self.goalIndex < len(self.goals) - 1:
rospy.sleep(self.goals[self.goalIndex][4])
self.goalIndex += 1
elif self.goalIndex == len(self.goals) - 1:
rospy.sleep(self.goals[self.goalIndex][4])
self.goalIndex = 1
class Swarm():
def __init__(self):
rospy.init_node('demo', anonymous=True)
self.worldFrame = rospy.get_param("~worldFrame", "/world")
self.frame = rospy.get_param("~frame")
self.pubGoal = rospy.Publisher('goal', PoseStamped, queue_size=1)
self.listener = TransformListener()
self.goals = goals
self.goalIndex = 1
self.index = 1
with open('test.csv','rb') as myfile:
reader=csv.reader(myfile)
lines = [line for line in reader]
def run(self):
self.listener.waitForTransform(self.worldFrame, self.frame, rospy.Time(), rospy.Duration(5.0))
goal = PoseStamped()
goal.header.seq = 0
goal.header.frame_id = self.worldFrame
while not rospy.is_shutdown():
goal.header.seq += 1
goal.header.stamp = rospy.Time.now()
goal.pose.position.x = int(lines[self.goalIndex][3*index-1])
goal.pose.position.y = int(lines[self.goalIndex][3*index+0])
goal.pose.position.z = int(lines[self.goalIndex][3*index+1])
quaternion = tf.transformations.quaternion_from_euler(0, 0, 0)
goal.pose.orientation.x = 0
goal.pose.orientation.y = 0
goal.pose.orientation.z = 0
goal.pose.orientation.w = 1
self.pubGoal.publish(goal)
t = self.listener.getLatestCommonTime(self.worldFrame, self.frame)
if self.listener.canTransform(self.worldFrame, self.frame, t):
position, quaternion = self.listener.lookupTransform(self.worldFrame, self.frame, t)
rpy = tf.transformations.euler_from_quaternion(quaternion)
if math.fabs(position[0] - int(lines[self.goalIndex][3*index-1])) < 0.25 \
and math.fabs(position[1] - int(lines[self.goalIndex][3*index+0])) < 0.25 \
and math.fabs(position[2] - int(lines[self.goalIndex][3*index+1])) < 0.25 \
and self.goalIndex < len(lines):
rospy.sleep(lines[self.goalIndex][1])
self.goalIndex += 1
class Demo:
def __init__(self, goals):
rospy.init_node("demo", anonymous=True)
self.frame = rospy.get_param("~frame")
self.pubGoal = rospy.Publisher("goal", PoseStamped, queue_size=1)
self.listener = TransformListener()
self.goals = goals
self.goalIndex = 0
def run(self):
self.listener.waitForTransform("/world", self.frame, rospy.Time(), rospy.Duration(5.0))
goal = PoseStamped()
goal.header.seq = 0
goal.header.frame_id = "world"
while not rospy.is_shutdown():
goal.header.seq += 1
goal.header.stamp = rospy.Time.now()
goal.pose.position.x = self.goals[self.goalIndex][0]
goal.pose.position.y = self.goals[self.goalIndex][1]
goal.pose.position.z = self.goals[self.goalIndex][2]
quaternion = tf.transformations.quaternion_from_euler(0, 0, self.goals[self.goalIndex][3])
goal.pose.orientation.x = quaternion[0]
goal.pose.orientation.y = quaternion[1]
goal.pose.orientation.z = quaternion[2]
goal.pose.orientation.w = quaternion[3]
self.pubGoal.publish(goal)
t = self.listener.getLatestCommonTime("/world", self.frame)
if self.listener.canTransform("/world", self.frame, t):
position, quaternion = self.listener.lookupTransform("/world", self.frame, t)
rpy = tf.transformations.euler_from_quaternion(quaternion)
if (
math.fabs(position[0] - self.goals[self.goalIndex][0]) < 0.3
and math.fabs(position[1] - self.goals[self.goalIndex][1]) < 0.3
and math.fabs(position[2] - self.goals[self.goalIndex][2]) < 0.3
and math.fabs(rpy[2] - self.goals[self.goalIndex][3]) < math.radians(10)
and self.goalIndex < len(self.goals) - 1
):
rospy.sleep(self.goals[self.goalIndex][4])
self.goalIndex += 1
class ParticleFilter(object):
"""
Class to represent Particle Filter ROS Node
Subscribes to /initialpose for initial pose estimate
Publishes top particle estimate to /particlebest and all particles in cloud to /particlecloud
"""
def __init__(self):
"""
__init__ function to create main attributes, setup threshold values, setup rosnode subs and pubs
"""
rospy.init_node('pf')
self.initialized = False
self.num_particles = 150
self.d_thresh = 0.2 # the amount of linear movement before performing an update
self.a_thresh = math.pi / 6 # the amount of angular movement before performing an update
self.particle_cloud = []
self.lidar_points = []
self.particle_pub = rospy.Publisher("particlecloud",
PoseArray,
queue_size=10)
self.best_particle_pub = rospy.Publisher("particlebest",
PoseStamped,
queue_size=10)
self.base_frame = "base_link" # the frame of the robot base
self.map_frame = "map" # the name of the map coordinate frame
self.odom_frame = "odom" # the name of the odometry coordinate frame
self.scan_topic = "scan" # the topic where we will get laser scans from
self.best_guess = (
None, None) # (index of particle with highest weight, its weight)
self.particles_to_replace = .075
self.n_effective = 0 # this is a measure of the particle diversity
# pose_listener responds to selection of a new approximate robot
# location (for instance using rviz)
rospy.Subscriber("initialpose", PoseWithCovarianceStamped,
self.update_initial_pose)
# enable listening for and broadcasting coordinate transforms
self.tf_listener = TransformListener()
self.tf_broadcaster = TransformBroadcaster()
# laser_subscriber listens for data from the lidar
rospy.Subscriber(self.scan_topic, LaserScan, self.scan_received)
# create instances of two helper objects that are provided to you
# as part of the project
self.current_odom_xy_theta = []
self.occupancy_field = OccupancyField()
self.transform_helper = TFHelper()
self.initialized = True
def update_initial_pose(self, msg):
""" Callback function to handle re-initializing the particle filter based on a pose estimate.
These pose estimates could be generated by another ROS Node or could come from the rviz GUI """
xy_theta = self.transform_helper.convert_pose_to_xy_and_theta(
msg.pose.pose)
print("xy_theta", xy_theta)
self.initialize_particle_cloud(
msg.header.stamp,
xy_theta) # creates particle cloud at position passed in
# by message
print("INITIALIZING POSE")
# Use the helper functions to fix the transform
def initialize_particle_cloud(self, timestamp, xy_theta):
"""
Creates initial particle cloud based on robot pose estimate position
"""
self.particle_cloud = []
angle_variance = math.pi / 10 # POint the points in the general direction of the robot
x_cur = xy_theta[0]
y_cur = xy_theta[1]
theta_cur = self.transform_helper.angle_normalize(xy_theta[2])
# print("theta_cur: ", theta_cur)
for i in range(self.num_particles):
# Generate values for and add a new particle!!
x_rel = random.uniform(-.3, .3)
y_rel = random.uniform(-.3, .3)
new_theta = (random.uniform(theta_cur - angle_variance,
theta_cur + angle_variance))
# TODO: Could use a tf transform to add x and y in the robot's coordinate system
new_particle = Particle(x_cur + x_rel, y_cur + y_rel, new_theta)
self.particle_cloud.append(new_particle)
print("Done initializing particles")
self.normalize_particles()
# publish particles (so things like rviz can see them)
self.publish_particles()
print("normalized correctly")
self.update_robot_pose(timestamp)
print("updated robot pose")
def normalize_particles(self):
"""
Normalizes particle weights to total but retains weightage
"""
total_weights = sum([particle.w for particle in self.particle_cloud])
# if your weights aren't normalized then normalize them
if total_weights != 1.0:
for i in self.particle_cloud:
i.w = i.w / total_weights
def update_robot_pose(self, timestamp):
""" Update the estimate of the robot's pose in the map frame given the updated particles.
There are two logical methods for this:
(1): compute the mean pose based on all the high weight particles
(2): compute the most likely pose (i.e. the mode of the distribution)
"""
# first make sure that the particle weights are normalized
self.normalize_particles()
print("Normalized particles in update robot pose")
# create average pose for robot pose based on entire particle cloud
average_x = 0
average_y = 0
average_theta = 0
# walk through all particles, calculate weighted average for x, y, z, in particle map.
for p in self.particle_cloud:
average_x += p.x * p.w
average_y += p.y * p.w
average_theta += p.theta * p.w
# # create new particle representing weighted average values, pass in Pose to new robot pose
self.robot_pose = Particle(average_x, average_y,
average_theta).as_pose()
print(timestamp)
self.transform_helper.fix_map_to_odom_transform(
self.robot_pose, timestamp)
print("Done fixing map to odom")
def publish_particles(self):
"""
Publish entire particle cloud as pose array for visualization in RVIZ
Also publish the top / best particle based on its weight
"""
# Convert the particles from xy_theta to pose!!
pose_particle_cloud = []
for p in self.particle_cloud:
pose_particle_cloud.append(p.as_pose())
self.particle_pub.publish(
PoseArray(header=Header(stamp=rospy.Time.now(),
frame_id=self.map_frame),
poses=pose_particle_cloud))
# doing shit based off best pose
best_pose_quat = max(self.particle_cloud,
key=attrgetter('w')).as_pose()
#self.best_particle_pub.publish(header=Header(stamp=rospy.Time.now(), frame_id=self.map_frame), pose=best_pose_quat)
def update_particles_with_odom(self, msg):
""" Update the particles using the newly given odometry pose.
The function computes the value delta which is a tuple (x,y,theta)
that indicates the change in position and angle between the odometry
when the particles were last updated and the current odometry.
msg: this is not really needed to implement this, but is here just in case.
"""
new_odom_xy_theta = self.transform_helper.convert_pose_to_xy_and_theta(
self.odom_pose.pose)
# compute the change in x,y,theta since our last update
if self.current_odom_xy_theta:
old_odom_xy_theta = self.current_odom_xy_theta
delta = (new_odom_xy_theta[0] - self.current_odom_xy_theta[0],
new_odom_xy_theta[1] - self.current_odom_xy_theta[1],
new_odom_xy_theta[2] - self.current_odom_xy_theta[2])
self.current_odom_xy_theta = new_odom_xy_theta
else:
self.current_odom_xy_theta = new_odom_xy_theta
return
# TODO: FIX noise incorporation into movement.
min_travel = 0.2
xy_spread = 0.02 / min_travel # More variance with driving forward
theta_spread = .005 / min_travel
random_vals_x = np.random.normal(0, abs(delta[0] * xy_spread),
self.num_particles)
random_vals_y = np.random.normal(0, abs(delta[1] * xy_spread),
self.num_particles)
random_vals_theta = np.random.normal(0, abs(delta[2] * theta_spread),
self.num_particles)
for p_num, p in enumerate(self.particle_cloud):
# compute phi, or basically the angle from 0 that the particle
# needs to be moving - phi equals OG diff angle - robot angle + OG partilce angle
# ADD THE NOISE!!
noisy_x = (delta[0] + random_vals_x[p_num])
noisy_y = (delta[1] + random_vals_y[p_num])
ang_of_dest = math.atan2(noisy_y, noisy_x)
# calculate angle needed to turn in angle_to_dest
ang_to_dest = self.transform_helper.angle_diff(
self.current_odom_xy_theta[2], ang_of_dest)
d = math.sqrt(noisy_x**2 + noisy_y**2)
phi = p.theta + ang_to_dest
p.x += math.cos(phi) * d
p.y += math.sin(phi) * d
p.theta += self.transform_helper.angle_normalize(
delta[2] + random_vals_theta[p_num])
self.current_odom_xy_theta = new_odom_xy_theta
def update_particles_with_laser(self, msg):
"""
calculate particle weights based off laser scan data passed into param
"""
# print("Updating particles with Laser")
lidar_points = msg.ranges
for p_deg, p in enumerate(self.particle_cloud):
# do we need to compute particle pos in diff frame?
p.occ_scan_mapped = [] # reset list
for scan_distance in lidar_points:
# handle edge case
if scan_distance == 0.0:
continue
# calc a delta theta and use that to overlay scan data onto the particle headings
pt_rad = deg2rad(p_deg)
particle_pt_theta = self.transform_helper.angle_normalize(
p.theta + pt_rad)
particle_pt_x = p.x + math.cos(
particle_pt_theta) * scan_distance
particle_pt_y = p.y + math.sin(
particle_pt_theta) * scan_distance
# calculate distance from every single scan point in particle frame
occ_value = self.occupancy_field.get_closest_obstacle_distance(
particle_pt_x, particle_pt_y)
# Think about cutting off max penalty if occ_value is too big
p.occ_scan_mapped.append(occ_value)
# assign weights based off newly assigned occ_scan_mapped
# apply gaussian e**-d**2 to every weight, then cube to emphasize
p.occ_scan_mapped = [(math.e / (d)**2) if (d)**2 != 0 else
(math.e / (d + .01)**2)
for d in p.occ_scan_mapped]
p.occ_scan_mapped = [d**3 for d in p.occ_scan_mapped]
p.w = sum(p.occ_scan_mapped)
#print("Set weight to: ", p.w)
p.occ_scan_mapped = []
self.normalize_particles()
@staticmethod
def draw_random_sample(choices, probabilities, n):
""" Return a random sample of n elements from the set choices with the specified probabilities
choices: the values to sample from represented as a list
probabilities: the probability of selecting each element in choices represented as a list
n: the number of samples
"""
values = np.array(range(len(choices)))
probs = np.array(probabilities)
bins = np.add.accumulate(probs)
inds = values[np.digitize(random_sample(n), bins)]
samples = []
for i in inds:
samples.append(deepcopy(choices[int(i)]))
return samples
def resample_particles(self):
"""
Re initialize particles in self.particle_cloud
based on preivous weightages.
"""
weights = [p.w for p in self.particle_cloud]
# after calculating all particle weights, we want to calc the n_effective
# self.n_effective = 0
self.n_effective = 1 / sum(
[w**2 for w in weights]) # higher is more diversity, so less noise
print("n_effective: ", self.n_effective)
temp_particle_cloud = self.draw_random_sample(
self.particle_cloud, weights,
int((1 - self.particles_to_replace) * self.num_particles))
# temp_particle_cloud = self.draw_random_sample(self.particle_cloud, weights, self.num_particles)
particle_cloud_to_transform = self.draw_random_sample(
self.particle_cloud, weights, self.num_particles - int(
(1 - self.particles_to_replace) * self.num_particles))
# NOISE POLLUTION - larger noise, smaller # particles
# normal_std_xy = .25
normal_std_xy = 10 / self.n_effective # feedback loop? 8,3
normal_std_theta = 3 / self.n_effective
# normal_std_theta = math.pi/21
random_vals_x = np.random.normal(0, normal_std_xy,
len(particle_cloud_to_transform))
random_vals_y = np.random.normal(0, normal_std_xy,
len(particle_cloud_to_transform))
random_vals_theta = np.random.normal(0, normal_std_theta,
len(particle_cloud_to_transform))
for p_num, p in enumerate(
particle_cloud_to_transform): # add in noise in x,y, theta
p.x += random_vals_x[p_num]
p.y += random_vals_y[p_num]
p.theta += random_vals_theta[p_num]
# reset the partilce cloud based on the newly transformed particles
self.particle_cloud = temp_particle_cloud + particle_cloud_to_transform
def scan_received(self, msg):
"""
Callback function for recieving laser scan - should pass data into global scan object
"""
""" This is the default logic for what to do when processing scan data.
Feel free to modify this, however, we hope it will provide a good
guide. The input msg is an object of type sensor_msgs/LaserScan """
if not (self.initialized):
# wait for initialization to complete
return
if not (self.tf_listener.canTransform(
self.base_frame, msg.header.frame_id, msg.header.stamp)):
# need to know how to transform the laser to the base frame
# this will be given by either Gazebo or neato_node
return
if not (self.tf_listener.canTransform(self.base_frame, self.odom_frame,
msg.header.stamp)):
# need to know how to transform between base and odometric frames
# this will eventually be published by either Gazebo or neato_node
return
# calculate pose of laser relative to the robot base
p = PoseStamped(
header=Header(stamp=rospy.Time(0), frame_id=msg.header.frame_id))
self.laser_pose = self.tf_listener.transformPose(self.base_frame, p)
# find out where the robot thinks it is based on its odometry
p = PoseStamped(header=Header(stamp=msg.header.stamp,
frame_id=self.base_frame),
pose=Pose())
# grab from listener & store the the odometry pose in a more convenient format (x,y,theta)
self.odom_pose = self.tf_listener.transformPose(self.odom_frame, p)
new_odom_xy_theta = self.transform_helper.convert_pose_to_xy_and_theta(
self.odom_pose.pose)
if not self.current_odom_xy_theta:
self.current_odom_xy_theta = new_odom_xy_theta
return
# Now we've done all calcs, we exit the scan_recieved() method by either initializing a cloud
if not (self.particle_cloud):
# now that we have all of the necessary transforms we can update the particle cloud
# TODO: Where do we get the xy_theta needed for initialize_particle_cloud?
self.initialize_particle_cloud(msg.header.stamp,
self.current_odom_xy_theta)
elif (math.fabs(new_odom_xy_theta[0] - self.current_odom_xy_theta[0]) >
self.d_thresh
or math.fabs(new_odom_xy_theta[1] -
self.current_odom_xy_theta[1]) > self.d_thresh
or math.fabs(new_odom_xy_theta[2] -
self.current_odom_xy_theta[2]) > self.a_thresh):
# we have moved far enough to do an update!
self.update_particles_with_odom(msg)
self.update_particles_with_laser(msg) # update based on laser scan
self.update_robot_pose(msg.header.stamp) # update robot's pose
self.resample_particles(
) # resample particles to focus on areas of high density
# # publish particles (so things like rviz can see them)
self.publish_particles()
def run(self):
"""
main run loop for rosnode
"""
r = rospy.Rate(5)
print("Nathan and Adi ROS Loop code is starting!!!")
while not (rospy.is_shutdown()):
# in the main loop all we do is continuously broadcast the latest
# map to odom transform
self.transform_helper.send_last_map_to_odom_transform()
r.sleep()
class ParticleFilter:
""" The class that represents a Particle Filter ROS Node
Attributes list:
initialized: a Boolean flag to communicate to other class methods that initializaiton is complete
base_frame: the name of the robot base coordinate frame (should be "base_link" for most robots)
map_frame: the name of the map coordinate frame (should be "map" in most cases)
odom_frame: the name of the odometry coordinate frame (should be "odom" in most cases)
scan_topic: the name of the scan topic to listen to (should be "scan" in most cases)
n_particles: the number of particles in the filter
d_thresh: the amount of linear movement before triggering a filter update
a_thresh: the amount of angular movement before triggering a filter update
laser_max_distance: the maximum distance to an obstacle we should use in a likelihood calculation
pose_listener: a subscriber that listens for new approximate pose estimates (i.e. generated through the rviz GUI)
particle_pub: a publisher for the particle cloud
laser_subscriber: listens for new scan data on topic self.scan_topic
tf_listener: listener for coordinate transforms
tf_broadcaster: broadcaster for coordinate transforms
particle_cloud: a list of particles representing a probability distribution over robot poses
current_odom_xy_theta: the pose of the robot in the odometry frame when the last filter update was performed.
The pose is expressed as a list [x,y,theta] (where theta is the yaw)
map: the map we will be localizing ourselves in. The map should be of type nav_msgs/OccupancyGrid
"""
def __init__(self):
self.initialized = False # make sure we don't perform updates before everything is setup
rospy.init_node(
'pf') # tell roscore that we are creating a new node named "pf"
self.base_frame = "base_link" # the frame of the robot base
self.map_frame = "map" # the name of the map coordinate frame
self.odom_frame = "odom" # the name of the odometry coordinate frame
self.scan_topic = "scan" # the topic where we will get laser scans from
self.n_particles = 300 # the number of particles to use
self.d_thresh = 0.2 # the amount of linear movement before performing an update
self.a_thresh = math.pi / 6 # the amount of angular movement before performing an update
self.laser_max_distance = 2.0 # maximum penalty to assess in the likelihood field model
# TODO: define additional constants if needed
# Setup pubs and subs
# pose_listener responds to selection of a new approximate robot location (for instance using rviz)
self.pose_listener = rospy.Subscriber("initialpose",
PoseWithCovarianceStamped,
self.update_initial_pose)
# publish the current particle cloud. This enables viewing particles in rviz.
self.particle_pub = rospy.Publisher("particlecloud",
PoseArray,
queue_size=10)
# laser_subscriber listens for data from the lidar
self.laser_subscriber = rospy.Subscriber(self.scan_topic, LaserScan,
self.scan_received)
# enable listening for and broadcasting coordinate transforms
self.tf_listener = TransformListener()
self.tf_broadcaster = TransformBroadcaster()
self.particle_cloud = []
self.current_odom_xy_theta = []
# request the map from the map server, the map should be of type nav_msgs/OccupancyGrid
# TODO: fill in the appropriate service call here. The resultant map should be assigned be passed
# into the init method for OccupancyField
# for now we have commented out the occupancy field initialization until you can successfully fetch the map
#self.occupancy_field = OccupancyField(map)
self.initialized = True
def update_robot_pose(self):
""" Update the estimate of the robot's pose given the updated particles.
There are two logical methods for this:
(1): compute the mean pose
(2): compute the most likely pose (i.e. the mode of the distribution)
"""
# first make sure that the particle weights are normalized
self.normalize_particles()
# TODO: assign the lastest pose into self.robot_pose as a geometry_msgs.Pose object
# just to get started we will fix the robot's pose to always be at the origin
self.robot_pose = Pose()
def update_particles_with_odom(self, msg):
""" Update the particles using the newly given odometry pose.
The function computes the value delta which is a tuple (x,y,theta)
that indicates the change in position and angle between the odometry
when the particles were last updated and the current odometry.
msg: this is not really needed to implement this, but is here just in case.
"""
new_odom_xy_theta = convert_pose_to_xy_and_theta(self.odom_pose.pose)
# compute the change in x,y,theta since our last update
if self.current_odom_xy_theta:
old_odom_xy_theta = self.current_odom_xy_theta
delta = (new_odom_xy_theta[0] - self.current_odom_xy_theta[0],
new_odom_xy_theta[1] - self.current_odom_xy_theta[1],
new_odom_xy_theta[2] - self.current_odom_xy_theta[2])
self.current_odom_xy_theta = new_odom_xy_theta
else:
self.current_odom_xy_theta = new_odom_xy_theta
return
# TODO: modify particles using delta
# For added difficulty: Implement sample_motion_odometry (Prob Rob p 136)
def map_calc_range(self, x, y, theta):
""" Difficulty Level 3: implement a ray tracing likelihood model... Let me know if you are interested """
# TODO: nothing unless you want to try this alternate likelihood model
pass
def resample_particles(self):
""" Resample the particles according to the new particle weights.
The weights stored with each particle should define the probability that a particular
particle is selected in the resampling step. You may want to make use of the given helper
function draw_random_sample.
"""
# make sure the distribution is normalized
self.normalize_particles()
# TODO: fill out the rest of the implementation
def update_particles_with_laser(self, msg):
""" Updates the particle weights in response to the scan contained in the msg """
# TODO: implement this
pass
@staticmethod
def weighted_values(values, probabilities, size):
""" Return a random sample of size elements from the set values with the specified probabilities
values: the values to sample from (numpy.ndarray)
probabilities: the probability of selecting each element in values (numpy.ndarray)
size: the number of samples
"""
bins = np.add.accumulate(probabilities)
return values[np.digitize(random_sample(size), bins)]
@staticmethod
def draw_random_sample(choices, probabilities, n):
""" Return a random sample of n elements from the set choices with the specified probabilities
choices: the values to sample from represented as a list
probabilities: the probability of selecting each element in choices represented as a list
n: the number of samples
"""
values = np.array(range(len(choices)))
probs = np.array(probabilities)
bins = np.add.accumulate(probs)
inds = values[np.digitize(random_sample(n), bins)]
samples = []
for i in inds:
samples.append(deepcopy(choices[int(i)]))
return samples
def update_initial_pose(self, msg):
""" Callback function to handle re-initializing the particle filter based on a pose estimate.
These pose estimates could be generated by another ROS Node or could come from the rviz GUI """
xy_theta = convert_pose_to_xy_and_theta(msg.pose.pose)
self.initialize_particle_cloud(xy_theta)
self.fix_map_to_odom_transform(msg)
def initialize_particle_cloud(self, xy_theta=None):
""" Initialize the particle cloud.
Arguments
xy_theta: a triple consisting of the mean x, y, and theta (yaw) to initialize the
particle cloud around. If this input is ommitted, the odometry will be used """
if xy_theta == None:
xy_theta = convert_pose_to_xy_and_theta(self.odom_pose.pose)
self.particle_cloud = []
# TODO create particles
self.normalize_particles()
self.update_robot_pose()
def normalize_particles(self):
""" Make sure the particle weights define a valid distribution (i.e. sum to 1.0) """
pass
# TODO: implement this
def publish_particles(self, msg):
particles_conv = []
for p in self.particle_cloud:
particles_conv.append(p.as_pose())
# actually send the message so that we can view it in rviz
self.particle_pub.publish(
PoseArray(header=Header(stamp=rospy.Time.now(),
frame_id=self.map_frame),
poses=particles_conv))
def scan_received(self, msg):
""" This is the default logic for what to do when processing scan data.
Feel free to modify this, however, I hope it will provide a good
guide. The input msg is an object of type sensor_msgs/LaserScan """
if not (self.initialized):
# wait for initialization to complete
return
if not (self.tf_listener.canTransform(
self.base_frame, msg.header.frame_id, msg.header.stamp)):
# need to know how to transform the laser to the base frame
# this will be given by either Gazebo or neato_node
return
if not (self.tf_listener.canTransform(self.base_frame, self.odom_frame,
msg.header.stamp)):
# need to know how to transform between base and odometric frames
# this will eventually be published by either Gazebo or neato_node
return
# calculate pose of laser relative ot the robot base
p = PoseStamped(
header=Header(stamp=rospy.Time(0), frame_id=msg.header.frame_id))
self.laser_pose = self.tf_listener.transformPose(self.base_frame, p)
# find out where the robot thinks it is based on its odometry
p = PoseStamped(header=Header(stamp=msg.header.stamp,
frame_id=self.base_frame),
pose=Pose())
self.odom_pose = self.tf_listener.transformPose(self.odom_frame, p)
# store the the odometry pose in a more convenient format (x,y,theta)
new_odom_xy_theta = convert_pose_to_xy_and_theta(self.odom_pose.pose)
if not (self.particle_cloud):
# now that we have all of the necessary transforms we can update the particle cloud
self.initialize_particle_cloud()
# cache the last odometric pose so we can only update our particle filter if we move more than self.d_thresh or self.a_thresh
self.current_odom_xy_theta = new_odom_xy_theta
# update our map to odom transform now that the particles are initialized
self.fix_map_to_odom_transform(msg)
elif (math.fabs(new_odom_xy_theta[0] - self.current_odom_xy_theta[0]) >
self.d_thresh
or math.fabs(new_odom_xy_theta[1] -
self.current_odom_xy_theta[1]) > self.d_thresh
or math.fabs(new_odom_xy_theta[2] -
self.current_odom_xy_theta[2]) > self.a_thresh):
# we have moved far enough to do an update!
self.update_particles_with_odom(msg) # update based on odometry
self.update_particles_with_laser(msg) # update based on laser scan
self.update_robot_pose() # update robot's pose
self.resample_particles(
) # resample particles to focus on areas of high density
self.fix_map_to_odom_transform(
msg
) # update map to odom transform now that we have new particles
# publish particles (so things like rviz can see them)
self.publish_particles(msg)
def fix_map_to_odom_transform(self, msg):
""" This method constantly updates the offset of the map and
odometry coordinate systems based on the latest results from
the localizer
TODO: if you want to learn a lot about tf, reimplement this... I can provide
you with some hints as to what is going on here. """
(translation,
rotation) = convert_pose_inverse_transform(self.robot_pose)
p = PoseStamped(pose=convert_translation_rotation_to_pose(
translation, rotation),
header=Header(stamp=msg.header.stamp,
frame_id=self.base_frame))
self.tf_listener.waitForTransform(self.base_frame,
self.odom_frame, msg.header.stamp,
rospy.Duration(1.0))
self.odom_to_map = self.tf_listener.transformPose(self.odom_frame, p)
(self.translation,
self.rotation) = convert_pose_inverse_transform(self.odom_to_map.pose)
def broadcast_last_transform(self):
""" Make sure that we are always broadcasting the last map
to odom transformation. This is necessary so things like
move_base can work properly. """
if not (hasattr(self, 'translation') and hasattr(self, 'rotation')):
return
self.tf_broadcaster.sendTransform(self.translation, self.rotation,
rospy.get_rostime(), self.odom_frame,
self.map_frame)
class ParticleFilter:
""" The class that represents a Particle Filter ROS Node
Attributes list:
initialized: a Boolean flag to communicate to other class methods that initializaiton is complete
base_frame: the name of the robot base coordinate frame (should be "base_link" for most robots)
map_frame: the name of the map coordinate frame (should be "map" in most cases)
odom_frame: the name of the odometry coordinate frame (should be "odom" in most cases)
scan_topic: the name of the scan topic to listen to (should be "scan" in most cases)
n_particles: the number of particles in the filter
d_thresh: the amount of linear movement before triggering a filter update
a_thresh: the amount of angular movement before triggering a filter update
laser_max_distance: the maximum distance to an obstacle we should use in a likelihood calculation
pose_listener: a subscriber that listens for new approximate pose estimates (i.e. generated through the rviz GUI)
particle_pub: a publisher for the particle cloud
laser_subscriber: listens for new scan data on topic self.scan_topic
tf_listener: listener for coordinate transforms
tf_broadcaster: broadcaster for coordinate transforms
particle_cloud: a list of particles representing a probability distribution over robot poses
current_odom_xy_theta: the pose of the robot in the odometry frame when the last filter update was performed.
The pose is expressed as a list [x,y,theta] (where theta is the yaw)
map: the map we will be localizing ourselves in. The map should be of type nav_msgs/OccupancyGrid
"""
def __init__(self):
self.initialized = False # make sure we don't perform updates before everything is setup
rospy.init_node(
'pf') # tell roscore that we are creating a new node named "pf"
self.base_frame = "base_link" # the frame of the robot base
self.map_frame = "map" # the name of the map coordinate frame
self.odom_frame = "odom" # the name of the odometry coordinate frame
self.scan_topic = "scan" # the topic where we will get laser scans from
self.n_particles = 300 # the number of particles to use
self.d_thresh = 0.2 # the amount of linear movement before performing an update
self.a_thresh = math.pi / 6 # the amount of angular movement before performing an update
self.laser_max_distance = 2.0 # maximum penalty to assess in the likelihood field model
# TODO: define additional constants if needed
#### DELETE BEFORE POSTING
self.alpha1 = 0.2
self.alpha2 = 0.2
self.alpha3 = 0.2
self.alpha4 = 0.2
self.z_hit = 0.5
self.z_rand = 0.5
self.sigma_hit = 0.1
##### DELETE BEFORE POSTING
# Setup pubs and subs
# pose_listener responds to selection of a new approximate robot location (for instance using rviz)
self.pose_listener = rospy.Subscriber("initialpose",
PoseWithCovarianceStamped,
self.update_initial_pose)
# publish the current particle cloud. This enables viewing particles in rviz.
self.particle_pub = rospy.Publisher("particlecloud", PoseArray)
# laser_subscriber listens for data from the lidar
self.laser_subscriber = rospy.Subscriber(self.scan_topic, LaserScan,
self.scan_received)
# enable listening for and broadcasting coordinate transforms
self.tf_listener = TransformListener()
self.tf_broadcaster = TransformBroadcaster()
self.particle_cloud = []
self.current_odom_xy_theta = []
# request the map
# Difficulty level 2
rospy.wait_for_service("static_map")
static_map = rospy.ServiceProxy("static_map", GetMap)
try:
map = static_map().map
except:
print "error receiving map"
self.occupancy_field = OccupancyField(map)
self.initialized = True
def update_robot_pose(self):
""" Update the estimate of the robot's pose given the updated particles.
There are two logical methods for this:
(1): compute the mean pose
(2): compute the most likely pose (i.e. the mode of the distribution)
"""
""" Difficulty level 2 """
# first make sure that the particle weights are normalized
self.normalize_particles()
use_mean = True
if use_mean:
mean_x = 0.0
mean_y = 0.0
mean_theta = 0.0
theta_list = []
weighted_orientation_vec = np.zeros((2, 1))
for p in self.particle_cloud:
mean_x += p.x * p.w
mean_y += p.y * p.w
weighted_orientation_vec[0] += p.w * math.cos(p.theta)
weighted_orientation_vec[1] += p.w * math.sin(p.theta)
mean_theta = math.atan2(weighted_orientation_vec[1],
weighted_orientation_vec[0])
self.robot_pose = Particle(x=mean_x, y=mean_y,
theta=mean_theta).as_pose()
else:
weights = []
for p in self.particle_cloud:
weights.append(p.w)
best_particle = np.argmax(weights)
self.robot_pose = self.particle_cloud[best_particle].as_pose()
def update_particles_with_odom(self, msg):
""" Implement a simple version of this (Level 1) or a more complex one (Level 2) """
new_odom_xy_theta = TransformHelpers.convert_pose_to_xy_and_theta(
self.odom_pose.pose)
if self.current_odom_xy_theta:
old_odom_xy_theta = self.current_odom_xy_theta
delta = (new_odom_xy_theta[0] - self.current_odom_xy_theta[0],
new_odom_xy_theta[1] - self.current_odom_xy_theta[1],
new_odom_xy_theta[2] - self.current_odom_xy_theta[2])
self.current_odom_xy_theta = new_odom_xy_theta
else:
self.current_odom_xy_theta = new_odom_xy_theta
return
# Implement sample_motion_odometry (Prob Rob p 136)
# Avoid computing a bearing from two poses that are extremely near each
# other (happens on in-place rotation).
delta_trans = math.sqrt(delta[0] * delta[0] + delta[1] * delta[1])
if delta_trans < 0.01:
delta_rot1 = 0.0
else:
delta_rot1 = ParticleFilter.angle_diff(
math.atan2(delta[1], delta[0]), old_odom_xy_theta[2])
delta_rot2 = ParticleFilter.angle_diff(delta[2], delta_rot1)
# We want to treat backward and forward motion symmetrically for the
# noise model to be applied below. The standard model seems to assume
# forward motion.
delta_rot1_noise = min(
math.fabs(ParticleFilter.angle_diff(delta_rot1, 0.0)),
math.fabs(ParticleFilter.angle_diff(delta_rot1, math.pi)))
delta_rot2_noise = min(
math.fabs(ParticleFilter.angle_diff(delta_rot2, 0.0)),
math.fabs(ParticleFilter.angle_diff(delta_rot2, math.pi)))
for sample in self.particle_cloud:
# Sample pose differences
delta_rot1_hat = ParticleFilter.angle_diff(
delta_rot1,
gauss(
0, self.alpha1 * delta_rot1_noise * delta_rot1_noise +
self.alpha2 * delta_trans * delta_trans))
delta_trans_hat = delta_trans - gauss(
0, self.alpha3 * delta_trans * delta_trans +
self.alpha4 * delta_rot1_noise * delta_rot1_noise +
self.alpha4 * delta_rot2_noise * delta_rot2_noise)
delta_rot2_hat = ParticleFilter.angle_diff(
delta_rot2,
gauss(
0, self.alpha1 * delta_rot2_noise * delta_rot2_noise +
self.alpha2 * delta_trans * delta_trans))
# Apply sampled update to particle pose
sample.x += delta_trans_hat * math.cos(sample.theta +
delta_rot1_hat)
sample.y += delta_trans_hat * math.sin(sample.theta +
delta_rot1_hat)
sample.theta += delta_rot1_hat + delta_rot2_hat
def map_calc_range(self, x, y, theta):
""" Difficulty Level 3: implement a ray tracing likelihood model... Let me know if you are interested """
pass
def resample_particles(self):
self.normalize_particles()
values = np.empty(self.n_particles)
probs = np.empty(self.n_particles)
for i in range(len(self.particle_cloud)):
values[i] = i
probs[i] = self.particle_cloud[i].w
new_particle_indices = ParticleFilter.weighted_values(
values, probs, self.n_particles)
new_particles = []
for i in new_particle_indices:
idx = int(i)
s_p = self.particle_cloud[idx]
new_particles.append(
Particle(x=s_p.x + gauss(0, .025),
y=s_p.y + gauss(0, .025),
theta=s_p.theta + gauss(0, .025)))
self.particle_cloud = new_particles
self.normalize_particles()
# Difficulty level 1
def update_particles_with_laser(self, msg):
""" Updates the particle weights in response to the scan contained in the msg """
laser_xy_theta = TransformHelpers.convert_pose_to_xy_and_theta(
self.laser_pose.pose)
for p in self.particle_cloud:
adjusted_pose = (p.x + laser_xy_theta[0], p.y + laser_xy_theta[1],
p.theta + laser_xy_theta[2])
# Pre-compute a couple of things
z_hit_denom = 2 * self.sigma_hit**2
z_rand_mult = 1.0 / msg.range_max
# This assumes quite a bit about the weights beforehand (TODO: could base this on p.w)
new_prob = 1.0 # more agressive DEBUG, was 1.0
for i in range(0, len(msg.ranges), 6):
pz = 1.0
obs_range = msg.ranges[i]
obs_bearing = i * msg.angle_increment + msg.angle_min
if math.isnan(obs_range):
continue
if obs_range >= msg.range_max:
continue
# compute the endpoint of the laser
end_x = p.x + obs_range * math.cos(p.theta + obs_bearing)
end_y = p.y + obs_range * math.sin(p.theta + obs_bearing)
z = self.occupancy_field.get_closest_obstacle_distance(
end_x, end_y)
if math.isnan(z):
z = self.laser_max_distance
else:
z = z[0] # not sure why this is happening
pz += self.z_hit * math.exp(-(z * z) / z_hit_denom) / (
math.sqrt(2 * math.pi) * self.sigma_hit)
pz += self.z_rand * z_rand_mult
new_prob += pz**3
p.w = new_prob
pass
@staticmethod
def angle_normalize(z):
""" convenience function to map an angle to the range [-pi,pi] """
return math.atan2(math.sin(z), math.cos(z))
@staticmethod
def angle_diff(a, b):
""" Calculates the difference between angle a and angle b (both should be in radians)
the difference is always based on the closest rotation from angle a to angle b
examples:
angle_diff(.1,.2) -> -.1
angle_diff(.1, 2*math.pi - .1) -> .2
angle_diff(.1, .2+2*math.pi) -> -.1
"""
a = ParticleFilter.angle_normalize(a)
b = ParticleFilter.angle_normalize(b)
d1 = a - b
d2 = 2 * math.pi - math.fabs(d1)
if d1 > 0:
d2 *= -1.0
if math.fabs(d1) < math.fabs(d2):
return d1
else:
return d2
@staticmethod
def weighted_values(values, probabilities, size):
""" Return a random sample of size elements form the set values with the specified probabilities
values: the values to sample from (numpy.ndarray)
probabilities: the probability of selecting each element in values (numpy.ndarray)
size: the number of samples
"""
bins = np.add.accumulate(probabilities)
return values[np.digitize(random_sample(size), bins)]
def update_initial_pose(self, msg):
""" Callback function to handle re-initializing the particle filter based on a pose estimate.
These pose estimates could be generated by another ROS Node or could come from the rviz GUI """
xy_theta = TransformHelpers.convert_pose_to_xy_and_theta(msg.pose.pose)
self.initialize_particle_cloud(xy_theta)
self.fix_map_to_odom_transform(msg)
def initialize_particle_cloud(self, xy_theta=None):
""" Initialize the particle cloud.
Arguments
xy_theta: a triple consisting of the mean x, y, and theta (yaw) to initialize the
particle cloud around. If this input is ommitted, the odometry will be used """
if xy_theta == None:
xy_theta = TransformHelpers.convert_pose_to_xy_and_theta(
self.odom_pose.pose)
self.particle_cloud = []
for i in range(self.n_particles):
self.particle_cloud.append(
Particle(x=xy_theta[0] + gauss(0, .25),
y=xy_theta[1] + gauss(0, .25),
theta=xy_theta[2] + gauss(0, .25)))
self.normalize_particles()
self.update_robot_pose()
""" Level 1 """
def normalize_particles(self):
""" Make sure the particle weights define a valid distribution (i.e. sum to 1.0) """
z = 0.0
for p in self.particle_cloud:
z += p.w
for i in range(len(self.particle_cloud)):
self.particle_cloud[i].w /= z
def publish_particles(self, msg):
particles_conv = []
for p in self.particle_cloud:
particles_conv.append(p.as_pose())
# actually send the message so that we can view it in rviz
self.particle_pub.publish(
PoseArray(header=Header(stamp=rospy.Time.now(),
frame_id=self.map_frame),
poses=particles_conv))
def scan_received(self, msg):
""" This is the default logic for what to do when processing scan data. Feel free to modify this, however,
I hope it will provide a good guide. The input msg is an object of type sensor_msgs/LaserScan """
if not (self.initialized):
# wait for initialization to complete
return
if not (self.tf_listener.canTransform(
self.base_frame, msg.header.frame_id, msg.header.stamp)):
# need to know how to transform the laser to the base frame
# this will be given by either Gazebo or neato_node
return
if not (self.tf_listener.canTransform(self.base_frame, self.odom_frame,
msg.header.stamp)):
# need to know how to transform between base and odometric frames
# this will eventually be published by either Gazebo or neato_node
return
# calculate pose of laser relative ot the robot base
p = PoseStamped(
header=Header(stamp=rospy.Time(0), frame_id=msg.header.frame_id))
self.laser_pose = self.tf_listener.transformPose(self.base_frame, p)
# find out where the robot thinks it is based on its odometry
p = PoseStamped(header=Header(stamp=msg.header.stamp,
frame_id=self.base_frame),
pose=Pose())
self.odom_pose = self.tf_listener.transformPose(self.odom_frame, p)
# store the the odometry pose in a more convenient format (x,y,theta)
new_odom_xy_theta = TransformHelpers.convert_pose_to_xy_and_theta(
self.odom_pose.pose)
if not (self.particle_cloud):
# now that we have all of the necessary transforms we can update the particle cloud
self.initialize_particle_cloud()
# cache the last odometric pose so we can only update our particle filter if we move more than self.d_thresh or self.a_thresh
self.current_odom_xy_theta = new_odom_xy_theta
# update our map to odom transform now that the particles are initialized
self.fix_map_to_odom_transform(msg)
elif (math.fabs(new_odom_xy_theta[0] - self.current_odom_xy_theta[0]) >
self.d_thresh
or math.fabs(new_odom_xy_theta[1] -
self.current_odom_xy_theta[1]) > self.d_thresh
or math.fabs(new_odom_xy_theta[2] -
self.current_odom_xy_theta[2]) > self.a_thresh):
# we have moved far enough to do an update!
self.update_particles_with_odom(msg) # update based on odometry
self.update_particles_with_laser(msg) # update based on laser scan
self.resample_particles(
) # resample particles to focus on areas of high density
self.update_robot_pose() # update robot's pose
self.fix_map_to_odom_transform(
msg
) # update map to odom transform now that we have new particles
# publish particles (so things like rviz can see them)
self.publish_particles(msg)
def fix_map_to_odom_transform(self, msg):
""" Super tricky code to properly update map to odom transform... do not modify this... Difficulty level infinity. """
(translation,
rotation) = TransformHelpers.convert_pose_inverse_transform(
self.robot_pose)
p = PoseStamped(
pose=TransformHelpers.convert_translation_rotation_to_pose(
translation, rotation),
header=Header(stamp=msg.header.stamp, frame_id=self.base_frame))
self.odom_to_map = self.tf_listener.transformPose(self.odom_frame, p)
(self.translation,
self.rotation) = TransformHelpers.convert_pose_inverse_transform(
self.odom_to_map.pose)
def broadcast_last_transform(self):
""" Make sure that we are always broadcasting the last map to odom transformation.
This is necessary so things like move_base can work properly. """
if not (hasattr(self, 'translation') and hasattr(self, 'rotation')):
return
self.tf_broadcaster.sendTransform(self.translation, self.rotation,
rospy.get_rostime(), self.odom_frame,
self.map_frame)
class ParticleFilter:
""" The class that represents a Particle Filter ROS Node
Attributes list:
initialized: a Boolean flag to communicate to other class methods that initializaiton is complete
base_frame: the name of the robot base coordinate frame (should be "base_link" for most robots)
map_frame: the name of the map coordinate frame (should be "map" in most cases)
odom_frame: the name of the odometry coordinate frame (should be "odom" in most cases)
scan_topic: the name of the scan topic to listen to (should be "scan" in most cases)
number_of_particles: the number of particles in the filter
d_thresh: the amount of linear movement before triggering a filter update
a_thresh: the amount of angular movement before triggering a filter update
laser_max_distance: the maximum distance to an obstacle we should use in a likelihood calculation
pose_listener: a subscriber that listens for new approximate pose estimates (i.e. generated through the rviz GUI)
particle_pub: a publisher for the particle cloud
laser_subscriber: listens for new scan data on topic self.scan_topic
tf_listener: listener for coordinate transforms
tf_broadcaster: broadcaster for coordinate transforms
particle_cloud: a list of particles representing a probability distribution over robot poses
current_odom_xy_theta: the pose of the robot in the odometry frame when the last filter update was performed.
The pose is expressed as a list [x,y,theta] (where theta is the yaw)
map: the map we will be localizing ourselves in. The map should be of type nav_msgs/OccupancyGrid
"""
def __init__(self):
self.initialized = False # make sure we don't perform updates before everything is setup
rospy.init_node('pf') # tell roscore that we are creating a new node named "pf"
self.base_frame = "base_link" # the frame of the robot base
self.map_frame = "map" # the name of the map coordinate frame
self.odom_frame = "odom" # the name of the odometry coordinate frame
self.scan_topic = "scan" # the topic where we will get laser scans from
self.number_of_particles = 1000 # the number of particles to use
self.d_thresh = 0.2 # the amount of linear movement before performing an update
self.a_thresh = math.pi/6 # the amount of angular movement before performing an update
self.laser_max_distance = 2.0 # maximum penalty to assess in the likelihood field model
# pose_listener responds to selection of a new approximate robot location (for instance using rviz)
self.pose_listener = rospy.Subscriber("initialpose", PoseWithCovarianceStamped, self.update_initial_pose)
# publish the current particle cloud. This enables viewing particles in rviz.
self.particle_pub = rospy.Publisher("particlecloud", PoseArray, queue_size=10)
# laser_subscriber listens for data from the lidar
self.laser_subscriber = rospy.Subscriber(self.scan_topic, LaserScan, self.scan_received)
# enable listening for and broadcasting coordinate transforms
self.tf_listener = TransformListener()
self.tf_broadcaster = TransformBroadcaster()
self.particle_cloud = []
self.current_odom_xy_theta = []
# Fetch map using OccupancyField
rospy.wait_for_service('static_map')
static_map = rospy.ServiceProxy('static_map', GetMap)
self.occupancy_field = OccupancyField(static_map().map)
self.initialized = True
def update_robot_pose(self):
""" Update the estimate of the robot's pose given the updated particles.
There are two logical methods for this:
(1): compute the mean pose
(2): compute the most likely pose (i.e. the mode of the distribution)
"""
# first make sure that the particle weights are normalized
self.normalize_particles()
avg_x = 0
avg_y = 0
theta_x = 0
theta_y = 0
# Multiple x and y by particle weights to find new robot pose
for particle in self.particle_cloud:
avg_x += particle.x * particle.w
avg_y += particle.y * particle.w
theta_x += math.cos(particle.theta) * particle.w
theta_y += math.sin(particle.theta) * particle.w
# Calculate theta using arc tan of x and y components of all thetas multiplied by particle weights
avg_theta = math.atan2(theta_y, theta_x)
avg_particle = Particle(x=avg_x, y=avg_y, theta=avg_theta)
# Update robot pose based on average particle
self.robot_pose = avg_particle.as_pose()
def update_particles_with_odom(self, msg):
""" Update the particles using the newly given odometry pose.
The function computes the value delta which is a tuple (x,y,theta)
that indicates the change in position and angle between the odometry
when the particles were last updated and the current odometry.
msg: this is not really needed to implement this, but is here just in case.
"""
new_odom_xy_theta = convert_pose_to_xy_and_theta(self.odom_pose.pose)
# compute the change in x,y,theta since our last update
if self.current_odom_xy_theta:
old_odom_xy_theta = self.current_odom_xy_theta
delta = (new_odom_xy_theta[0] - self.current_odom_xy_theta[0],
new_odom_xy_theta[1] - self.current_odom_xy_theta[1],
new_odom_xy_theta[2] - self.current_odom_xy_theta[2])
self.current_odom_xy_theta = new_odom_xy_theta
else:
self.current_odom_xy_theta = new_odom_xy_theta
return
temp = []
# Use trigonometry to update particles based on new odometry pose
for particle in self.particle_cloud:
psy = math.atan2(delta[1],delta[0])-old_odom_xy_theta[2]
intermediate_theta = particle.theta + psy
# Calculate radius based on change in x and y
r = math.sqrt(delta[0]**2 + delta[1]**2)
# Update x and y based on radius and new angle
new_x = particle.x + r*math.cos(intermediate_theta) + np.random.randn()*0.1
new_y = particle.y + r*math.sin(intermediate_theta) + np.random.randn()*0.1
# Add change in angle to old angle
new_theta = delta[2]+particle.theta + np.random.randn()*0.1
temp.append(Particle(new_x,new_y,new_theta))
self.particle_cloud = temp
def map_calc_range(self,x,y,theta):
""" Difficulty Level 3: implement a ray tracing likelihood model... Let me know if you are interested """
# TODO: nothing unless you want to try this alternate likelihood model
pass
def resample_particles(self):
""" Resample the particles according to the new particle weights.
The weights stored with each particle should define the probability that a particular
particle is selected in the resampling step. You may want to make use of the given helper
function draw_random_sample.
"""
probabilities = []
# create list of particle weights to pass into draw_random_sample for resampling
for particle in self.particle_cloud:
probabilities.append(particle.w)
print particle.w
print '\n'
temp_particle_cloud = self.draw_random_sample(self.particle_cloud, probabilities, 100)
self.particle_cloud = []
for particle in temp_particle_cloud:
for i in range(10):
self.particle_cloud.append(deepcopy(particle))
self.normalize_particles()
def update_particles_with_laser(self, msg):
""" Updates the particle weights in response to the scan contained in the msg """
temp = 0
ranges = []
min_range = 5
for item in msg.ranges:
# set ranges to 5 if the laser scan is 0
if item == 0:
ranges.append(5)
else:
ranges.append(item)
# do weighted averages for cleaner data
for i in range(355):
avg = sum(ranges[i:i+5]) / len(ranges[i:i+5])
if avg < min_range:
min_range = avg
min_theta = (i + 2.5)*math.pi / 180.0
# find the minimum range across 360 angles, this probably caused an issue
r = min_range
# Update particle x, y, theta based on min range, previous particles
for particle in self.particle_cloud:
x = particle.x+r*math.cos(particle.theta + min_theta)
y = particle.y+r*math.sin(particle.theta + min_theta)
temp = self.occupancy_field.get_closest_obstacle_distance(x,y)
# Update particle weights using a sharp Gaussian
particle.w = np.exp(-np.power(temp, 2.) / (2 * np.power(0.3, 2.)))
@staticmethod
def weighted_values(values, probabilities, size):
""" Return a random sample of size elements from the set values with the specified probabilities
values: the values to sample from (numpy.ndarray)
probabilities: the probability of selecting each element in values (numpy.ndarray)
size: the number of samples
"""
bins = np.add.accumulate(probabilities)
return values[np.digitize(random_sample(size), bins)]
@staticmethod
def draw_random_sample(choices, probabilities, n):
""" Return a random sample of n elements from the set choices with the specified probabilities
choices: the values to sample from represented as a list
probabilities: the probability of selecting each element in choices represented as a list
n: the number of samples
"""
values = np.array(range(len(choices)))
probs = np.array(probabilities)
bins = np.add.accumulate(probs)
inds = values[np.digitize(random_sample(n), bins)]
samples = []
for i in inds:
samples.append(deepcopy(choices[int(i)]))
return samples
def update_initial_pose(self, msg):
""" Callback function to handle re-initializing the particle filter based on a pose estimate.
These pose estimates could be generated by another ROS Node or could come from the rviz GUI """
xy_theta = convert_pose_to_xy_and_theta(msg.pose.pose)
self.initialize_particle_cloud(xy_theta)
self.fix_map_to_odom_transform(msg)
def initialize_particle_cloud(self, xy_theta=None):
""" Initialize the particle cloud.
Arguments
xy_theta: a triple consisting of the mean x, y, and theta (yaw) to initialize the
particle cloud around. If this input is ommitted, the odometry will be used """
if xy_theta == None:
xy_theta = convert_pose_to_xy_and_theta(self.odom_pose.pose)
self.particle_cloud = []
self.particle_cloud.append(Particle(xy_theta[0],xy_theta[1],xy_theta[2]))
# Initialize particle cloud with a decent amount of noise
for i in range (0,self.number_of_particles):
self.particle_cloud.append(Particle(xy_theta[0]+np.random.randn()*.5,xy_theta[1]+np.random.randn()*.5,xy_theta[2]+np.random.randn()*.5))
self.normalize_particles()
self.update_robot_pose()
def normalize_particles(self):
""" Make sure the particle weights define a valid distribution (i.e. sum to 1.0) """
particle_sum = 0
# Sum up particle weights to divide by for normalization
for particle in self.particle_cloud:
particle_sum += particle.w
# Make all particle weights add to 1
for particle in self.particle_cloud:
particle.w /= particle_sum
def publish_particles(self, msg):
particles_conv = []
for p in self.particle_cloud:
particles_conv.append(p.as_pose())
# actually send the message so that we can view it in rviz
self.particle_pub.publish(PoseArray(header=Header(stamp=rospy.Time.now(),
frame_id=self.map_frame),
poses=particles_conv))
def scan_received(self, msg):
""" This is the default logic for what to do when processing scan data.
Feel free to modify this, however, I hope it will provide a good
guide. The input msg is an object of type sensor_msgs/LaserScan """
if not(self.initialized):
# wait for initialization to complete
return
if not(self.tf_listener.canTransform(self.base_frame,msg.header.frame_id,msg.header.stamp)):
# need to know how to transform the laser to the base frame
# this will be given by either Gazebo or neato_node
return
if not(self.tf_listener.canTransform(self.base_frame,self.odom_frame,msg.header.stamp)):
# need to know how to transform between base and odometric frames
# this will eventually be published by either Gazebo or neato_node
return
# calculate pose of laser relative ot the robot base
p = PoseStamped(header=Header(stamp=rospy.Time(0),
frame_id=msg.header.frame_id))
self.laser_pose = self.tf_listener.transformPose(self.base_frame,p)
# find out where the robot thinks it is based on its odometry
p = PoseStamped(header=Header(stamp=msg.header.stamp,
frame_id=self.base_frame),
pose=Pose())
self.odom_pose = self.tf_listener.transformPose(self.odom_frame, p)
# store the the odometry pose in a more convenient format (x,y,theta)
new_odom_xy_theta = convert_pose_to_xy_and_theta(self.odom_pose.pose)
if not(self.particle_cloud):
# now that we have all of the necessary transforms we can update the particle cloud
self.initialize_particle_cloud()
# cache the last odometric pose so we can only update our particle filter if we move more than self.d_thresh or self.a_thresh
self.current_odom_xy_theta = new_odom_xy_theta
# update our map to odom transform now that the particles are initialized
self.fix_map_to_odom_transform(msg)
elif (math.fabs(new_odom_xy_theta[0] - self.current_odom_xy_theta[0]) > self.d_thresh or
math.fabs(new_odom_xy_theta[1] - self.current_odom_xy_theta[1]) > self.d_thresh or
math.fabs(new_odom_xy_theta[2] - self.current_odom_xy_theta[2]) > self.a_thresh):
# we have moved far enough to do an update!
self.update_particles_with_odom(msg) # update based on odometry
self.update_particles_with_laser(msg) # update based on laser scan
self.update_robot_pose() # update robot's pose
self.resample_particles() # resample particles to focus on areas of high density
self.fix_map_to_odom_transform(msg) # update map to odom transform now that we have new particles
# publish particles (so things like rviz can see them)
self.publish_particles(msg)
def fix_map_to_odom_transform(self, msg):
""" This method constantly updates the offset of the map and
odometry coordinate systems based on the latest results from
the localizer """
(translation, rotation) = convert_pose_inverse_transform(self.robot_pose)
p = PoseStamped(pose=convert_translation_rotation_to_pose(translation,rotation),
header=Header(stamp=msg.header.stamp,frame_id=self.base_frame))
self.odom_to_map = self.tf_listener.transformPose(self.odom_frame, p)
(self.translation, self.rotation) = convert_pose_inverse_transform(self.odom_to_map.pose)
def broadcast_last_transform(self):
""" Make sure that we are always broadcasting the last map
to odom transformation. This is necessary so things like
move_base can work properly. """
if not(hasattr(self,'translation') and hasattr(self,'rotation')):
return
self.tf_broadcaster.sendTransform(self.translation,
self.rotation,
rospy.get_rostime(),
self.odom_frame,
self.map_frame)
class MyAMCL:
def __init__(self):
self.initialized = False
rospy.init_node('my_amcl')
print "MY AMCL initialized"
# todo make this static
self.n_particles = 100
self.alpha1 = 0.2
self.alpha2 = 0.2
self.alpha3 = 0.2
self.alpha4 = 0.2
self.d_thresh = 0.2
self.a_thresh = math.pi/6
self.z_hit = 0.5
self.z_rand = 0.5
self.sigma_hit = 0.2
self.laser_max_distance = 2.0
self.laser_subscriber = rospy.Subscriber("scan", LaserScan, self.scan_received);
self.tf_listener = TransformListener()
self.tf_broadcaster = TransformBroadcaster()
self.particle_pub = rospy.Publisher("particlecloud", PoseArray)
self.particle_cloud = []
self.last_transform_valid = False
self.particle_cloud_initialized = False
self.current_odom_xy_theta = []
# request the map
rospy.wait_for_service("static_map")
static_map = rospy.ServiceProxy("static_map", GetMap)
try:
self.map = static_map().map
except:
print "error receiving map"
self.create_occupancy_field()
self.initialized = True
def create_occupancy_field(self):
X = np.zeros((self.map.info.width*self.map.info.height,2))
total_occupied = 0
curr = 0
for i in range(self.map.info.width):
for j in range(self.map.info.height):
# occupancy grids are stored in row major order, if you go through this right, you might be able to use curr
ind = i + j*self.map.info.width
if self.map.data[ind] > 0:
total_occupied += 1
X[curr,0] = float(i)
X[curr,1] = float(j)
curr += 1
O = np.zeros((total_occupied,2))
curr = 0
for i in range(self.map.info.width):
for j in range(self.map.info.height):
# occupancy grids are stored in row major order, if you go through this right, you might be able to use curr
ind = i + j*self.map.info.width
if self.map.data[ind] > 0:
O[curr,0] = float(i)
O[curr,1] = float(j)
curr += 1
t = time.time()
nbrs = NearestNeighbors(n_neighbors=1,algorithm="ball_tree").fit(O)
distances, indices = nbrs.kneighbors(X)
print time.time() -t
closest_occ = {}
curr = 0
for i in range(self.map.info.width):
for j in range(self.map.info.height):
ind = i + j*self.map.info.width
closest_occ[ind] = distances[curr]*self.map.info.resolution
curr += 1
# this is a bit adhoc, could probably integrate into an internal map structure
self.closest_occ = closest_occ
def update_robot_pose(self):
# first make sure that the particle weights are normalized
self.normalize_particles()
use_mean = True
if use_mean:
mean_x = 0.0
mean_y = 0.0
mean_theta = 0.0
theta_list = []
weighted_orientation_vec = np.zeros((2,1))
for p in self.particle_cloud:
mean_x += p.x*p.w
mean_y += p.y*p.w
weighted_orientation_vec[0] += p.w*math.cos(p.theta)
weighted_orientation_vec[1] += p.w*math.sin(p.theta)
mean_theta = math.atan2(weighted_orientation_vec[1],weighted_orientation_vec[0])
self.robot_pose = Particle(x=mean_x,y=mean_y,theta=mean_theta).as_pose()
else:
weights = []
for p in self.particle_cloud:
weights.append(p.w)
best_particle = np.argmax(weights)
self.robot_pose = self.particle_cloud[best_particle].as_pose()
def update_particles_with_odom(self, msg):
new_odom_xy_theta = MyAMCL.convert_pose_to_xy_and_theta(self.odom_pose.pose)
if self.current_odom_xy_theta:
old_odom_xy_theta = self.current_odom_xy_theta
delta = (new_odom_xy_theta[0] - self.current_odom_xy_theta[0], new_odom_xy_theta[1] - self.current_odom_xy_theta[1], new_odom_xy_theta[2] - self.current_odom_xy_theta[2])
self.current_odom_xy_theta = new_odom_xy_theta
else:
self.current_odom_xy_theta = new_odom_xy_theta
return
# Implement sample_motion_odometry (Prob Rob p 136)
# Avoid computing a bearing from two poses that are extremely near each
# other (happens on in-place rotation).
delta_trans = math.sqrt(delta[0]*delta[0] + delta[1]*delta[1])
if delta_trans < 0.01:
delta_rot1 = 0.0
else:
delta_rot1 = MyAMCL.angle_diff(math.atan2(delta[1], delta[0]), old_odom_xy_theta[2])
delta_rot2 = MyAMCL.angle_diff(delta[2], delta_rot1)
# We want to treat backward and forward motion symmetrically for the
# noise model to be applied below. The standard model seems to assume
# forward motion.
delta_rot1_noise = min(math.fabs(MyAMCL.angle_diff(delta_rot1,0.0)), math.fabs(MyAMCL.angle_diff(delta_rot1, math.pi)));
delta_rot2_noise = min(math.fabs(MyAMCL.angle_diff(delta_rot2,0.0)), math.fabs(MyAMCL.angle_diff(delta_rot2, math.pi)));
for sample in self.particle_cloud:
# Sample pose differences
delta_rot1_hat = MyAMCL.angle_diff(delta_rot1, gauss(0, self.alpha1*delta_rot1_noise*delta_rot1_noise + self.alpha2*delta_trans*delta_trans))
delta_trans_hat = delta_trans - gauss(0, self.alpha3*delta_trans*delta_trans + self.alpha4*delta_rot1_noise*delta_rot1_noise + self.alpha4*delta_rot2_noise*delta_rot2_noise)
delta_rot2_hat = MyAMCL.angle_diff(delta_rot2, gauss(0, self.alpha1*delta_rot2_noise*delta_rot2_noise + self.alpha2*delta_trans*delta_trans))
# Apply sampled update to particle pose
sample.x += delta_trans_hat * math.cos(sample.theta + delta_rot1_hat)
sample.y += delta_trans_hat * math.sin(sample.theta + delta_rot1_hat)
sample.theta += delta_rot1_hat + delta_rot2_hat
def get_map_index(self,x,y):
x_coord = int((x - self.map.info.origin.position.x)/self.map.info.resolution)
y_coord = int((y - self.map.info.origin.position.y)/self.map.info.resolution)
# check if we are in bounds
if x_coord > self.map.info.width or x_coord < 0:
return float('nan')
if y_coord > self.map.info.height or y_coord < 0:
return float('nan')
ind = x_coord + y_coord*self.map.info.width
if ind >= self.map.info.width*self.map.info.height or ind < 0:
return float('nan')
return ind
def map_calc_range(self,x,y,theta):
''' this is for a beam model... this is pretty damn slow...'''
(x_curr,y_curr) = (x,y)
ind = self.get_map_index(x_curr, y_curr)
while not(math.isnan(ind)):
if self.map.data[ind] > 0:
return math.sqrt((x - x_curr)**2 + (y - y_curr)**2)
x_curr += self.map.info.resolution*0.5*math.cos(theta)
y_curr += self.map.info.resolution*0.5*math.sin(theta)
ind = self.get_map_index(x_curr, y_curr)
if math.isnan(ind):
return float('nan')
else:
return self.map.info.range_max
def resample_particles(self):
self.normalize_particles()
values = np.empty(self.n_particles)
probs = np.empty(self.n_particles)
for i in range(len(self.particle_cloud)):
values[i] = i
probs[i] = self.particle_cloud[i].w
new_particle_indices = MyAMCL.weighted_values(values,probs,self.n_particles)
new_particles = []
for i in new_particle_indices:
idx = int(i)
s_p = self.particle_cloud[idx]
new_particles.append(Particle(x=s_p.x+gauss(0,.025),y=s_p.y+gauss(0,.025),theta=s_p.theta+gauss(0,.025)))
self.particle_cloud = new_particles
self.normalize_particles()
def update_particles_with_laser(self, msg):
laser_xy_theta = MyAMCL.convert_pose_to_xy_and_theta(self.laser_pose.pose)
for p in self.particle_cloud:
adjusted_pose = (p.x+laser_xy_theta[0], p.y+laser_xy_theta[1], p.theta+laser_xy_theta[2])
# Pre-compute a couple of things
z_hit_denom = 2*self.sigma_hit**2
z_rand_mult = 1.0/msg.range_max
# This assumes quite a bit about the weights beforehand (TODO: could base this on p.w)
new_prob = 1.0
for i in range(5,len(msg.ranges),10):
pz = 1.0
obs_range = msg.ranges[i]
obs_bearing = i*msg.angle_increment+msg.angle_min
if math.isnan(obs_range):
continue
if obs_range >= msg.range_max:
continue
# compute the endpoint of the laser
end_x = p.x + obs_range*math.cos(p.theta+obs_bearing)
end_y = p.y + obs_range*math.sin(p.theta+obs_bearing)
ind = self.get_map_index(end_x,end_y)
if math.isnan(ind):
z = self.laser_max_distance
else:
z = self.closest_occ[ind]
pz += self.z_hit * math.exp(-(z * z) / z_hit_denom)
pz += self.z_rand * z_rand_mult
new_prob += pz**3
p.w = new_prob
@staticmethod
def normalize(z):
return math.atan2(math.sin(z), math.cos(z))
@staticmethod
def angle_diff(a, b):
a = MyAMCL.normalize(a)
b = MyAMCL.normalize(b)
d1 = a-b
d2 = 2*math.pi - math.fabs(d1)
if d1 > 0:
d2 *= -1.0
if math.fabs(d1) < math.fabs(d2):
return d1
else:
return d2
@staticmethod
def weighted_values(values, probabilities, size):
bins = np.add.accumulate(probabilities)
return values[np.digitize(random_sample(size), bins)]
@staticmethod
def convert_pose_to_xy_and_theta(pose):
orientation_tuple = (pose.orientation.x, pose.orientation.y, pose.orientation.z, pose.orientation.w)
angles = euler_from_quaternion(orientation_tuple)
return (pose.position.x, pose.position.y, angles[2])
def initialize_particle_cloud(self):
self.particle_cloud_initialized = True
(x,y,theta) = MyAMCL.convert_pose_to_xy_and_theta(self.odom_pose.pose)
for i in range(self.n_particles):
self.particle_cloud.append(Particle(x=x+gauss(0,.25),y=y+gauss(0,.25),theta=theta+gauss(0,.25)))
self.normalize_particles()
def normalize_particles(self):
z = 0.0
for p in self.particle_cloud:
z += p.w
for i in range(len(self.particle_cloud)):
self.particle_cloud[i].w /= z
def publish_particles(self, msg):
particles_conv = []
for p in self.particle_cloud:
particles_conv.append(p.as_pose())
# actually send the message so that we can view it in rviz
self.particle_pub.publish(PoseArray(header=Header(stamp=rospy.Time.now(),frame_id="map"),poses=particles_conv))
def scan_received(self, msg):
if not(self.initialized):
return
if not(self.tf_listener.canTransform("base_footprint",msg.header.frame_id,msg.header.stamp)):
return
if not(self.tf_listener.canTransform("base_footprint","odom",msg.header.stamp)):
return
p = PoseStamped(header=Header(stamp=rospy.Time(0),frame_id=msg.header.frame_id))
self.laser_pose = self.tf_listener.transformPose("base_footprint",p)
p = PoseStamped(header=Header(stamp=msg.header.stamp,frame_id="base_footprint"), pose=Pose())
#p = PoseStamped(header=Header(stamp=msg.header.stamp,frame_id="base_footprint"), pose=Pose(position=Point(x=0.0,y=0.0,z=0.0),orientation=Quaternion(x=0.0,y=0.0,z=0.0,w=0.0)))
self.odom_pose = self.tf_listener.transformPose("odom", p)
new_odom_xy_theta = MyAMCL.convert_pose_to_xy_and_theta(self.odom_pose.pose)
if not(self.particle_cloud_initialized):
self.initialize_particle_cloud()
self.update_robot_pose()
self.current_odom_xy_theta = new_odom_xy_theta
self.fix_map_to_odom_transform(msg)
else:
delta = (new_odom_xy_theta[0] - self.current_odom_xy_theta[0], new_odom_xy_theta[1] - self.current_odom_xy_theta[1], new_odom_xy_theta[2] - self.current_odom_xy_theta[2])
if math.fabs(delta[0]) > self.d_thresh or math.fabs(delta[1]) > self.d_thresh or math.fabs(delta[2]) > self.a_thresh:
self.update_particles_with_odom(msg)
self.update_robot_pose()
self.update_particles_with_laser(msg)
self.resample_particles()
self.update_robot_pose()
self.fix_map_to_odom_transform(msg)
else:
self.fix_map_to_odom_transform(msg, False)
self.publish_particles(msg)
def fix_map_to_odom_transform(self, msg, recompute_odom_to_map=True):
if recompute_odom_to_map:
(translation, rotation) = MyAMCL.convert_pose_inverse_transform(self.robot_pose)
p = PoseStamped(pose=MyAMCL.convert_translation_rotation_to_pose(translation,rotation),header=Header(stamp=msg.header.stamp,frame_id="base_footprint"))
self.odom_to_map = self.tf_listener.transformPose("odom", p)
(translation, rotation) = MyAMCL.convert_pose_inverse_transform(self.odom_to_map.pose)
self.tf_broadcaster.sendTransform(translation, rotation, msg.header.stamp+rospy.Duration(1.0), "odom", "map")
@staticmethod
def convert_translation_rotation_to_pose(translation,rotation):
return Pose(position=Point(x=translation[0],y=translation[1],z=translation[2]), orientation=Quaternion(x=rotation[0],y=rotation[1],z=rotation[2],w=rotation[3]))
@staticmethod
def convert_pose_inverse_transform(pose):
translation = np.zeros((4,1))
translation[0] = -pose.position.x
translation[1] = -pose.position.y
translation[2] = -pose.position.z
translation[3] = 1.0
rotation = (pose.orientation.x, pose.orientation.y, pose.orientation.z, pose.orientation.w)
euler_angle = euler_from_quaternion(rotation)
rotation = np.transpose(rotation_matrix(euler_angle[2], [0,0,1])) # the angle is a yaw
transformed_translation = rotation.dot(translation)
translation = (transformed_translation[0], transformed_translation[1], transformed_translation[2])
rotation = quaternion_from_matrix(rotation)
return (translation, rotation)
class ParticleFilter:
""" The class that represents a Particle Filter ROS Node
Attributes list:
initialized: a Boolean flag to communicate to other class methods that initializaiton is complete
base_frame: the name of the robot base coordinate frame (should be "base_link" for most robots)
map_frame: the name of the map coordinate frame (should be "map" in most cases)
odom_frame: the name of the odometry coordinate frame (should be "odom" in most cases)
scan_topic: the name of the scan topic to listen to (should be "scan" in most cases)
n_particles: the number of particles in the filter
d_thresh: the amount of linear movement before triggering a filter update
a_thresh: the amount of angular movement before triggering a filter update
laser_max_distance: the maximum distance to an obstacle we should use in a likelihood calculation
pose_listener: a subscriber that listens for new approximate pose estimates (i.e. generated through the rviz GUI)
particle_pub: a publisher for the particle cloud
laser_subscriber: listens for new scan data on topic self.scan_topic
tf_listener: listener for coordinate transforms
tf_broadcaster: broadcaster for coordinate transforms
particle_cloud: a list of particles representing a probability distribution over robot poses
current_odom_xy_theta: the pose of the robot in the odometry frame when the last filter update was performed.
The pose is expressed as a list [x,y,theta] (where theta is the yaw)
map: the map we will be localizing ourselves in. The map should be of type nav_msgs/OccupancyGrid
"""
def __init__(self):
self.initialized = False # make sure we don't perform updates before everything is setup
rospy.init_node('pf') # tell roscore that we are creating a new node named "pf"
self.base_frame = "base_link" # the frame of the robot base
self.map_frame = "map" # the name of the map coordinate frame
self.odom_frame = "odom" # the name of the odometry coordinate frame
self.scan_topic = "base_scan" # the topic where we will get laser scans from
self.n_particles = 500 # the number of particles to use
self.d_thresh = 0.2 # the amount of linear movement before performing an update
self.a_thresh = math.pi/6 # the amount of angular movement before performing an update
self.laser_max_distance = 2.0 # maximum penalty to assess in the likelihood field model
self.sigma = 0.08 # guess for how inaccurate lidar readings are in meters
# Setup pubs and subs
# pose_listener responds to selection of a new approximate robot location (for instance using rviz)
self.pose_listener = rospy.Subscriber("initialpose", PoseWithCovarianceStamped, self.update_initial_pose)
# publish the current particle cloud. This enables viewing particles in rviz.
self.particle_pub = rospy.Publisher("particlecloud", PoseArray, queue_size=10)
self.marker_pub = rospy.Publisher("markers", MarkerArray, queue_size=10)
# laser_subscriber listens for data from the lidar
self.laser_subscriber = rospy.Subscriber(self.scan_topic, LaserScan, self.scan_received)
# enable listening for and broadcasting coordinate transforms
self.tf_listener = TransformListener()
self.tf_broadcaster = TransformBroadcaster()
self.particle_cloud = []
self.current_odom_xy_theta = []
# request the map from the map server, the map should be of type nav_msgs/OccupancyGrid
self.map_server = rospy.ServiceProxy('static_map', GetMap)
self.map = self.map_server().map
# for now we have commented out the occupancy field initialization until you can successfully fetch the map
self.occupancy_field = OccupancyField(self.map)
self.initialized = True
def update_robot_pose(self):
""" Update the estimate of the robot's pose given the updated particles.
Computed by taking the weighted average of poses.
"""
# first make sure that the particle weights are normalized
self.normalize_particles()
x = 0
y = 0
theta = 0
angles = []
for particle in self.particle_cloud:
x += particle.x * particle.w
y += particle.y * particle.w
v = [particle.w * math.cos(math.radians(particle.theta)), particle.w * math.sin(math.radians(particle.theta))]
angles.append(v)
theta = sum_vectors(angles)
orientation_tuple = tf.transformations.quaternion_from_euler(0,0,theta)
self.robot_pose = Pose(position=Point(x=x,y=y),orientation=Quaternion(x=orientation_tuple[0], y=orientation_tuple[1], z=orientation_tuple[2], w=orientation_tuple[3]))
def update_particles_with_odom(self, msg):
""" Update the particles using the newly given odometry pose.
The function computes the value delta which is a tuple (x,y,theta)
that indicates the change in position and angle between the odometry
when the particles were last updated and the current odometry.
msg: this is not really needed to implement this, but is here just in case.
"""
new_odom_xy_theta = convert_pose_to_xy_and_theta(self.odom_pose.pose)
# compute the change in x,y,theta since our last update
if self.current_odom_xy_theta:
old_odom_xy_theta = self.current_odom_xy_theta
delta = (new_odom_xy_theta[0] - self.current_odom_xy_theta[0],
new_odom_xy_theta[1] - self.current_odom_xy_theta[1],
new_odom_xy_theta[2] - self.current_odom_xy_theta[2])
self.current_odom_xy_theta = new_odom_xy_theta
else:
self.current_odom_xy_theta = new_odom_xy_theta
return
for particle in self.particle_cloud:
r1 = math.atan2(delta[1], delta[0]) - old_odom_xy_theta[2]
d = math.sqrt((delta[0]**2) + (delta[1]**2))
particle.theta += r1 % 360
particle.x += d * math.cos(particle.theta) + normal(0,0.1)
particle.y += d * math.sin(particle.theta) + normal(0,0.1)
particle.theta += (delta[2] - r1 + normal(0,0.1)) % 360
# For added difficulty: Implement sample_motion_odometry (Prob Rob p 136)
def map_calc_range(self,x,y,theta):
""" Difficulty Level 3: implement a ray tracing likelihood model... Let me know if you are interested """
pass
def resample_particles(self):
""" Resample the particles according to the new particle weights.
The weights stored with each particle should define the probability that a particular
particle is selected in the resampling step. You may want to make use of the given helper
function draw_random_sample.
"""
# make sure the distribution is normalized
self.normalize_particles()
newParticles = []
for i in range(len(self.particle_cloud)):
# resample the same # of particles
choice = random_sample()
# all the particle weights sum to 1
csum = 0 # cumulative sum
for particle in self.particle_cloud:
csum += particle.w
if csum >= choice:
# if the random choice fell within the particle's weight
newParticles.append(deepcopy(particle))
break
self.particle_cloud = newParticles
def update_particles_with_laser(self, msg):
""" Updates the particle weights in response to the scan contained in the msg """
for particle in self.particle_cloud:
tot_prob = 0
for index, scan in enumerate(msg.ranges):
x,y = self.transform_scan(particle,scan,index)
# transform scan to view of the particle
d = self.occupancy_field.get_closest_obstacle_distance(x,y)
# calculate nearest distance to particle's scan (should be near 0 if it's on robot)
tot_prob += math.exp((-d**2)/(2*self.sigma**2))
# add probability (0 to 1) to total probability
tot_prob = tot_prob/len(msg.ranges)
# normalize total probability back to 0-1
particle.w = tot_prob
# assign particles weight
def transform_scan(self, particle, distance, theta):
""" Calculates the x and y of a scan from a given particle
particle: Particle object
distance: scan distance (from ranges)
theta: scan angle (range index)
"""
return (particle.x + distance * math.cos(math.radians(particle.theta + theta)),
particle.y + distance * math.sin(math.radians(particle.theta + theta)))
@staticmethod
def weighted_values(values, probabilities, size):
""" Return a random sample of size elements from the set values with the specified probabilities
values: the values to sample from (numpy.ndarray)
probabilities: the probability of selecting each element in values (numpy.ndarray)
size: the number of samples
"""
bins = np.add.accumulate(probabilities)
return values[np.digitize(random_sample(size), bins)]
@staticmethod
def draw_random_sample(choices, probabilities, n):
""" Return a random sample of n elements from the set choices with the specified probabilities
choices: the values to sample from represented as a list
probabilities: the probability of selecting each element in choices represented as a list
n: the number of samples
"""
values = np.array(range(len(choices)))
probs = np.array(probabilities)
bins = np.add.accumulate(probs)
inds = values[np.digitize(random_sample(n), bins)]
samples = []
for i in inds:
samples.append(deepcopy(choices[int(i)]))
return samples
def update_initial_pose(self, msg):
""" Callback function to handle re-initializing the particle filter based on a pose estimate.
These pose estimates could be generated by another ROS Node or could come from the rviz GUI """
xy_theta = convert_pose_to_xy_and_theta(msg.pose.pose)
self.initialize_particle_cloud(xy_theta)
self.fix_map_to_odom_transform(msg)
def initialize_particle_cloud(self, xy_theta=None):
""" Initialize the particle cloud.
Arguments
xy_theta: a triple consisting of the mean x, y, and theta (yaw) to initialize the
particle cloud around. If this input is ommitted, the odometry will be used """
if xy_theta == None:
xy_theta = convert_pose_to_xy_and_theta(self.odom_pose.pose)
rad = 1 # meters
self.particle_cloud = []
self.particle_cloud.append(Particle(xy_theta[0], xy_theta[1], xy_theta[2]))
for i in range(self.n_particles-1):
# initial facing of the particle
theta = random.random() * 360
# compute params to generate x,y in a circle
other_theta = random.random() * 360
radius = random.random() * rad
# x => straight ahead
x = radius * math.sin(other_theta) + xy_theta[0]
y = radius * math.cos(other_theta) + xy_theta[1]
particle = Particle(x,y,theta)
self.particle_cloud.append(particle)
self.normalize_particles()
self.update_robot_pose()
def normalize_particles(self):
""" Make sure the particle weights define a valid distribution (i.e. sum to 1.0) """
tot_weight = sum([particle.w for particle in self.particle_cloud]) or 1
for particle in self.particle_cloud:
particle.w = particle.w/tot_weight;
def publish_particles(self, msg):
particles_conv = []
for p in self.particle_cloud:
particles_conv.append(p.as_pose())
# actually send the message so that we can view it in rviz
self.particle_pub.publish(PoseArray(header=Header(stamp=rospy.Time.now(),
frame_id=self.map_frame),
poses=particles_conv))
marker_array = []
for index, particle in enumerate(self.particle_cloud):
marker = Marker(header=Header(stamp=rospy.Time.now(),
frame_id=self.map_frame),
pose=particle.as_pose(),
type=0,
scale=Vector3(x=particle.w*2,y=particle.w*1,z=particle.w*5),
id=index,
color=ColorRGBA(r=1,a=1))
marker_array.append(marker)
self.marker_pub.publish(MarkerArray(markers=marker_array))
def scan_received(self, msg):
""" This is the default logic for what to do when processing scan data.
Feel free to modify this, however, I hope it will provide a good
guide. The input msg is an object of type sensor_msgs/LaserScan """
if not(self.initialized):
# wait for initialization to complete
return
if not(self.tf_listener.canTransform(self.base_frame,msg.header.frame_id,msg.header.stamp)):
# need to know how to transform the laser to the base frame
# this will be given by either Gazebo or neato_node
return
if not(self.tf_listener.canTransform(self.base_frame,self.odom_frame,msg.header.stamp)):
# need to know how to transform between base and odometric frames
# this will eventually be published by either Gazebo or neato_node
return
# calculate pose of laser relative ot the robot base
p = PoseStamped(header=Header(stamp=rospy.Time(0),
frame_id=msg.header.frame_id))
self.laser_pose = self.tf_listener.transformPose(self.base_frame,p)
# find out where the robot thinks it is based on its odometry
p = PoseStamped(header=Header(stamp=msg.header.stamp,
frame_id=self.base_frame),
pose=Pose())
self.odom_pose = self.tf_listener.transformPose(self.odom_frame, p)
# store the the odometry pose in a more convenient format (x,y,theta)
new_odom_xy_theta = convert_pose_to_xy_and_theta(self.odom_pose.pose)
if not(self.particle_cloud):
# now that we have all of the necessary transforms we can update the particle cloud
self.initialize_particle_cloud()
# cache the last odometric pose so we can only update our particle filter if we move more than self.d_thresh or self.a_thresh
self.current_odom_xy_theta = new_odom_xy_theta
# update our map to odom transform now that the particles are initialized
self.fix_map_to_odom_transform(msg)
elif (math.fabs(new_odom_xy_theta[0] - self.current_odom_xy_theta[0]) > self.d_thresh or
math.fabs(new_odom_xy_theta[1] - self.current_odom_xy_theta[1]) > self.d_thresh or
math.fabs(new_odom_xy_theta[2] - self.current_odom_xy_theta[2]) > self.a_thresh):
# we have moved far enough to do an update!
self.update_particles_with_odom(msg) # update based on odometry
self.update_particles_with_laser(msg) # update based on laser scan
self.update_robot_pose() # update robot's pose
self.resample_particles() # resample particles to focus on areas of high density
self.fix_map_to_odom_transform(msg) # update map to odom transform now that we have new particles
# publish particles (so things like rviz can see them)
self.publish_particles(msg)
def fix_map_to_odom_transform(self, msg):
""" This method constantly updates the offset of the map and
odometry coordinate systems based on the latest results from
the localizer """
(translation, rotation) = convert_pose_inverse_transform(self.robot_pose)
p = PoseStamped(pose=convert_translation_rotation_to_pose(translation,rotation),
header=Header(stamp=msg.header.stamp,frame_id=self.base_frame))
self.odom_to_map = self.tf_listener.transformPose(self.odom_frame, p)
(self.translation, self.rotation) = convert_pose_inverse_transform(self.odom_to_map.pose)
def broadcast_last_transform(self):
""" Make sure that we are always broadcasting the last map
to odom transformation. This is necessary so things like
move_base can work properly. """
if not(hasattr(self,'translation') and hasattr(self,'rotation')):
return
self.tf_broadcaster.sendTransform(self.translation,
self.rotation,
rospy.get_rostime(),
self.odom_frame,
self.map_frame)
class ParticleFilter:
def __init__(self):
# once everything is setup initialized will be set to true
self.initialized = False
# initialize this particle filter node
rospy.init_node('turtlebot3_particle_filter')
# set the topic names and frame names
self.base_frame = "base_footprint"
self.map_topic = "map"
self.odom_frame = "odom"
self.scan_topic = "scan"
# inialize our map
self.map = OccupancyGrid()
# create LikelihoodField object
self.likelihood_field = LikelihoodField()
# the number of particles used in the particle filter
self.num_particles = 10000
# initialize the particle cloud array
self.particle_cloud = []
# initialize the estimated robot pose
self.robot_estimate = Pose()
# set threshold values for linear and angular movement before we preform an update
self.lin_mvmt_threshold = 0.2
self.ang_mvmt_threshold = (np.pi / 6)
self.odom_pose_last_motion_update = None
# Setup publishers and subscribers
# publish the current particle cloud
self.particles_pub = rospy.Publisher("particle_cloud", PoseArray, queue_size=10)
# publish the estimated robot pose
self.robot_estimate_pub = rospy.Publisher("estimated_robot_pose", PoseStamped, queue_size=10)
# subscribe to the map server
rospy.Subscriber(self.map_topic, OccupancyGrid, self.get_map)
# subscribe to the lidar scan from the robot
rospy.Subscriber(self.scan_topic, LaserScan, self.robot_scan_received)
# enable listening for and broadcasting corodinate transforms
self.tf_listener = TransformListener()
self.tf_broadcaster = TransformBroadcaster()
# sleep to get map data before initializing particle cloud
rospy.sleep(1)
# intialize the particle cloud
self.initialize_particle_cloud()
self.initialized = True
def get_map(self, data):
self.map = data
def initialize_particle_cloud(self):
""" Initialize the particle cloud with random locations and
orientations throughout the house """
# get map data and random indices that correspond to coordinates
# with a light gray color (inside the house)
map_data = self.map.data
random_indices = draw_random_sample(self.num_particles, map_data, 0)
# initialize variables to convert from a particle's position to
# its index on the Occupancy Grid
r = self.map.info.resolution
x = self.map.info.origin.position.x
y = self.map.info.origin.position.y
w = self.map.info.width
h = self.map.info.height
for i in range(self.num_particles):
# set pose data for particle
p = Pose()
p.position = Point()
p.position.x = (random_indices[i] % w) * r + x
p.position.y = (random_indices[i] // h) * r + y
p.position.z = 0
p.orientation = Quaternion()
q = quaternion_from_euler(0.0, 0.0, math.radians(360 * random_sample()))
p.orientation.x = q[0]
p.orientation.y = q[1]
p.orientation.z = q[2]
p.orientation.w = q[3]
# initialize the new particle, where all will have the same weight (1.0)
new_particle = Particle(p, 1.0)
# append the particle to the particle cloud
self.particle_cloud.append(new_particle)
self.normalize_particles()
self.publish_particle_cloud()
def normalize_particles(self):
""" Normalize the particle weights so they add up to 1 """
# initialize sum variable to normalize
sum = 0
# add up all the particle weights into sum
for part in self.particle_cloud:
sum += part.w
# reassign weights by dividing each particle's original weight by sum
for part in self.particle_cloud:
part.w = part.w / sum
return
def publish_particle_cloud(self):
particle_cloud_pose_array = PoseArray()
particle_cloud_pose_array.header = Header(stamp=rospy.Time.now(), frame_id=self.map_topic)
particle_cloud_pose_array.poses
for part in self.particle_cloud:
particle_cloud_pose_array.poses.append(part.pose)
self.particles_pub.publish(particle_cloud_pose_array)
def publish_estimated_robot_pose(self):
robot_pose_estimate_stamped = PoseStamped()
robot_pose_estimate_stamped.pose = self.robot_estimate
robot_pose_estimate_stamped.header = Header(stamp=rospy.Time.now(), frame_id=self.map_topic)
self.robot_estimate_pub.publish(robot_pose_estimate_stamped)
def resample_particles(self):
""" Resample particles with probabilities proportionate to their weights """
# create an array of particle weights
particle_weights = []
for part in self.particle_cloud:
particle_weights.append(part.w)
# sample particles with probabilities proportinate to their weights
new_particle_cloud = choice(self.particle_cloud, self.num_particles, p = particle_weights)
# deepcopy these newly sampled particles into particle_cloud
for i in range(self.num_particles):
self.particle_cloud[i] = deepcopy(new_particle_cloud[i])
return
def robot_scan_received(self, data):
# wait until initialization is complete
if not(self.initialized):
return
# we need to be able to transfrom the laser frame to the base frame
if not(self.tf_listener.canTransform(self.base_frame, data.header.frame_id, data.header.stamp)):
return
# wait for a little bit for the transform to become avaliable (in case the scan arrives
# a little bit before the odom to base_footprint transform was updated)
self.tf_listener.waitForTransform(self.base_frame, self.odom_frame, data.header.stamp, rospy.Duration(0.5))
if not(self.tf_listener.canTransform(self.base_frame, data.header.frame_id, data.header.stamp)):
return
# calculate the pose of the laser distance sensor
p = PoseStamped(
header=Header(stamp=rospy.Time(0),
frame_id=data.header.frame_id))
self.laser_pose = self.tf_listener.transformPose(self.base_frame, p)
# determine where the robot thinks it is based on its odometry
p = PoseStamped(
header=Header(stamp=data.header.stamp,
frame_id=self.base_frame),
pose=Pose())
self.odom_pose = self.tf_listener.transformPose(self.odom_frame, p)
# we need to be able to compare the current odom pose to the prior odom pose
# if there isn't a prior odom pose, set the odom_pose variable to the current pose
if not self.odom_pose_last_motion_update:
self.odom_pose_last_motion_update = self.odom_pose
return
if self.particle_cloud:
# check to see if we've moved far enough to perform an update
curr_x = self.odom_pose.pose.position.x
old_x = self.odom_pose_last_motion_update.pose.position.x
curr_y = self.odom_pose.pose.position.y
old_y = self.odom_pose_last_motion_update.pose.position.y
curr_yaw = get_yaw_from_pose(self.odom_pose.pose)
old_yaw = get_yaw_from_pose(self.odom_pose_last_motion_update.pose)
if (np.abs(curr_x - old_x) > self.lin_mvmt_threshold or
np.abs(curr_y - old_y) > self.lin_mvmt_threshold or
np.abs(curr_yaw - old_yaw) > self.ang_mvmt_threshold):
# This is where the main logic of the particle filter is carried out
self.update_particles_with_motion_model()
self.update_particle_weights_with_measurement_model(data)
self.normalize_particles()
self.resample_particles()
self.update_estimated_robot_pose()
self.publish_particle_cloud()
self.publish_estimated_robot_pose()
self.odom_pose_last_motion_update = self.odom_pose
def update_estimated_robot_pose(self):
""" Based on the particles within the particle cloud, update the
robot pose estimate """
# initialize sum variables to average
px = py = pz = 0
ox = oy = oz = ow = 0
for part in self.particle_cloud:
p = part.pose
px += p.position.x
py += p.position.y
pz += p.position.z
ox += p.orientation.x
oy += p.orientation.y
oz += p.orientation.z
ow += p.orientation.w
# update estimated robot pose by taking the average of all particle poses
num = len(self.particle_cloud)
self.robot_estimate.position.x = px/num
self.robot_estimate.position.y = py/num
self.robot_estimate.position.z = pz/num
self.robot_estimate.orientation.x = ox/num
self.robot_estimate.orientation.y = oy/num
self.robot_estimate.orientation.z = oz/num
self.robot_estimate.orientation.w = ow/num
return
def update_particle_weights_with_measurement_model(self, data):
""" Update the particle weights using the likelihood field for
range finders model """
# wait until initialization is complete
if not(self.initialized):
return
# take into account 8 directions of data
cardinal_directions_idxs = [0, 45, 90, 135, 180, 225, 270, 315]
# compute the new probabilities for all particles based on model
for part in self.particle_cloud:
q = 1
for ang in cardinal_directions_idxs:
ztk = data.ranges[ang]
# if an detection is out of range, skip this angle
if ztk > data.range_max:
continue
theta = get_yaw_from_pose(part.pose)
x_ztk = part.pose.position.x + ztk * math.cos(theta + math.radians(ang))
y_ztk = part.pose.position.y + ztk * math.sin(theta + math.radians(ang))
dist = self.likelihood_field.get_closest_obstacle_distance(x_ztk, y_ztk)
# if (x_ztk,y_ztk) is out of the map boundaries
if math.isnan(dist):
prob = 0.00001
else:
prob = compute_prob_zero_centered_gaussian(dist, 0.1)
# update total probability assuming z_hit=0.8, z_err=z_max=0.1
q = q * (0.8 * prob + 1)
part.w = q
return
def update_particles_with_motion_model(self):
""" Calculates how much the robot has moved using odometry and move
all the particles correspondingly by the same amount with noise """
# calculate how the robot has moved
curr_x = self.odom_pose.pose.position.x
old_x = self.odom_pose_last_motion_update.pose.position.x
dx = curr_x - old_x
curr_y = self.odom_pose.pose.position.y
old_y = self.odom_pose_last_motion_update.pose.position.y
dy = curr_y - old_y
curr_yaw = get_yaw_from_pose(self.odom_pose.pose)
old_yaw = get_yaw_from_pose(self.odom_pose_last_motion_update.pose)
dyaw = curr_yaw - old_yaw
# move all the particles correspondingly with errors
for part in self.particle_cloud:
p = part.pose
p.position.x += add_error(dx)
p.position.y += add_error(dy)
new_yaw = get_yaw_from_pose(p) + add_error(dyaw)
q = quaternion_from_euler(0.0, 0.0, new_yaw)
p.orientation.x = q[0]
p.orientation.y = q[1]
p.orientation.z = q[2]
p.orientation.w = q[3]
return
class Collector():
def __init__(self):
rospy.init_node('collector', anonymous = False)
self.lis=TransformListener();
self.data_out=SBC_Output();
rospy.Subscriber("/joint_states", JointState, self.j_callback)
rospy.Subscriber("/finger1/ContactState", KCL_ContactStateStamped, self.f_callback1)
rospy.Subscriber("/finger2/ContactState", KCL_ContactStateStamped, self.f_callback2)
rospy.Subscriber("/pressure", PressureControl, self.p_callback)
rospy.Subscriber("/prob_fail", Float64, self.prob_callback)
self.publisher=rospy.Publisher("sbc_data", SBC_Output)
self.point1=PointStamped()
self.point2=PointStamped()
self.rate=rospy.Rate(20);
def getParams(self):
self.data_out.D_Gain=rospy.get_param("/bhand_pid/d_gain")
self.data_out.F_ref_pid=rospy.get_param("/bhand_pid/f_ref")
self.data_out.I_Gain=rospy.get_param("/bhand_pid/i_gain")
self.data_out.P_Gain=rospy.get_param("/bhand_pid/p_gain")
self.data_out.freq=rospy.get_param("/pressure_reg/frequency")
self.data_out.Beta=rospy.get_param("/bhand_sbc/beta")
self.data_out.Delta=rospy.get_param("/bhand_sbc/delta")
self.data_out.Eta=rospy.get_param("/bhand_sbc/eta")
self.data_out.F_ref_sbc=rospy.get_param("/bhand_sbc/f_ref")
def j_callback(self,data):
self.joints=data;
self.data_out.effort1=data.effort[1]
self.data_out.effort2=data.effort[0]
def f_callback1(self,data):
self.data_out.Fn1=data.Fnormal;
ft=np.array([data.tangential_force.x,data.tangential_force.y,data.tangential_force.z])
self.data_out.Ft1=np.sqrt(ft.dot(ft));
self.point1=PointStamped();
self.point1.header=data.header;
self.point1.point=data.contact_position;
def f_callback2(self,data):
self.data_out.Fn2=data.Fnormal;
ft=np.array([data.tangential_force.x,data.tangential_force.y,data.tangential_force.z])
self.data_out.Ft2=np.sqrt(ft.dot(ft));
self.point2=PointStamped();
self.point2.header=data.header;
self.point2.point=data.contact_position;
def p_callback(self,data):
self.data_out.p_demand=data.p_demand;
self.data_out.p_measure=data.p_measure;
def prob_callback(self,data):
self.data_out.Pfailure=data.data;
def transform_it(self,data):
if(data.header.frame_id):
#data.header.stamp=rospy.Time.now();
if(self.lis.canTransform("base_link",data.header.frame_id,data.header.stamp) or True):
#print(rospy.Time.now())
data.header.stamp=data.header.stamp-rospy.Duration(0.02)
#point=self.lis.transformPoint("base_link", data)
try:
#self.lis.waitForTransform("base_link", data.header.frame_id, data.header.stamp, rospy.Duration(1))
# print(rospy.Time.now())
self.point=self.lis.transformPoint("base_link", data)
return True
except (tf.LookupException, tf.ConnectivityException, tf.ExtrapolationException):
rospy.logwarn("TF problem 2")
pass
else:
rospy.logwarn("Cannot Transform")
else:
print(data.header.frame_id)
return False
def get_distance(self,point1,point2):
d=np.array([point1.x-point2.x, point1.y-point2.y, point1.z-point2.z])
return np.sqrt(d.dot(d));
def send_it(self):
while not rospy.is_shutdown():
self.data_out.header.stamp=rospy.Time.now();
self.getParams()
got_it=self.transform_it(self.point1);
if(got_it):
self.data_out.contact1=self.point.point
got_it=self.transform_it(self.point2);
if(got_it):
self.data_out.contact2=self.point.point
self.data_out.distance=self.get_distance(self.data_out.contact1,self.data_out.contact2)*100
self.publisher.publish(self.data_out);
self.rate.sleep();
class ParticleFilter(object):
""" The class that represents a Particle Filter ROS Node
Attributes list:
initialized: a Boolean flag to communicate to other class methods that initializaiton is complete
base_frame: the name of the robot base coordinate frame (should be "base_link" for most robots)
map_frame: the name of the map coordinate frame (should be "map" in most cases)
odom_frame: the name of the odometry coordinate frame (should be "odom" in most cases)
scan_topic: the name of the scan topic to listen to (should be "scan" in most cases)
n_particles: the number of particles in the filter
d_thresh: the amount of linear movement before triggering a filter update
a_thresh: the amount of angular movement before triggering a filter update
laser_max_distance: the maximum distance to an obstacle we should use in a likelihood calculation
pose_listener: a subscriber that listens for new approximate pose estimates (i.e. generated through the rviz GUI)
particle_pub: a publisher for the particle cloud
laser_subscriber: listens for new scan data on topic self.scan_topic
tf_listener: listener for coordinate transforms
tf_broadcaster: broadcaster for coordinate transforms
particle_cloud: a list of particles representing a probability distribution over robot poses
current_odom_xy_theta: the pose of the robot in the odometry frame when the last filter update was performed.
The pose is expressed as a list [x,y,theta] (where theta is the yaw)
map: the map we will be localizing ourselves in. The map should be of type nav_msgs/OccupancyGrid
"""
def __init__(self):
self.initialized = False # make sure we don't perform updates before everything is setup
rospy.init_node('pf') # tell roscore that we are creating a new node named "pf"
self.base_frame = "base_link" # the frame of the robot base
self.map_frame = "map" # the name of the map coordinate frame
self.odom_frame = "odom" # the name of the odometry coordinate frame
self.scan_topic = "scan" # the topic where we will get laser scans from
self.n_particles = 300 # the number of particles to use
self.p_lost = .4 # The probability given to the robot being "lost" at any given time
self.outliers_to_keep = int(self.n_particles * self.p_lost * 0.5) # The number of outliers to keep around
self.d_thresh = 0.2 # the amount of linear movement before performing an update
self.a_thresh = math.pi / 6 # the amount of angular movement before performing an update
self.laser_max_distance = 2.0 # maximum penalty to assess in the likelihood field model
# TODO: define additional constants if needed
# Setup pubs and subs
# pose_listener responds to selection of a new approximate robot location (for instance using rviz)
self.pose_listener = rospy.Subscriber("initialpose", PoseWithCovarianceStamped, self.update_initial_pose)
# publish the current particle cloud. This enables viewing particles in rviz.
self.particle_pub = rospy.Publisher("particlecloud", PoseArray, queue_size=10)
# laser_subscriber listens for data from the lidar
self.laser_subscriber = rospy.Subscriber(self.scan_topic, LaserScan, self.scan_received)
# enable listening for and broadcasting coordinate transforms
self.tf_listener = TransformListener()
self.tf_broadcaster = TransformBroadcaster()
self.particle_cloud = []
self.current_odom_xy_theta = []
# Make a ros service call to the /static_map service to get a nav_msgs/OccupancyGrid map.
# Then use OccupancyField to make the map object
robotMap = rospy.ServiceProxy('/static_map', GetMap)().map
self.occupancy_field = OccupancyField(robotMap)
print "OccupancyField initialized", self.occupancy_field
self.initialized = True
def update_robot_pose(self):
""" Update the estimate of the robot's pose given the updated particles.
There are two logical methods for this:
(1): compute the mean pose
(2): compute the most likely pose (i.e. the mode of the distribution)
Our strategy is #2 to enable better tracking of unlikely particles in the future
"""
# first make sure that the particle weights are normalized
self.normalize_particles()
chosen_one = max(self.particle_cloud, key=lambda p: p.w)
# TODO: assign the lastest pose into self.robot_pose as a geometry_msgs.Pose object
# just to get started we will fix the robot's pose to always be at the origin
self.robot_pose = chosen_one.as_pose()
def update_particles_with_odom(self, msg):
""" Update the particles using the newly given odometry pose.
The function computes the value delta which is a tuple (x,y,theta)
that indicates the change in position and angle between the odometry
when the particles were last updated and the current odometry.
msg: this is not really needed to implement this, but is here just in case.
"""
new_odom_xy_theta = convert_pose_to_xy_and_theta(self.odom_pose.pose)
# compute the change in x,y,theta since our last update
if self.current_odom_xy_theta:
old_odom_xy_theta = self.current_odom_xy_theta
delta = (new_odom_xy_theta[0] - self.current_odom_xy_theta[0],
new_odom_xy_theta[1] - self.current_odom_xy_theta[1],
angle_diff(new_odom_xy_theta[2], self.current_odom_xy_theta[2]))
self.current_odom_xy_theta = new_odom_xy_theta
else:
self.current_odom_xy_theta = new_odom_xy_theta
return
for i, particle in enumerate(self.particle_cloud):
# TODO: Change odometry uncertainty to be ROS param
# Calculate the angle difference between the old odometry position
# and the old particle position. Then create a rotation matrix between
# the two angles
rotationmatrix = self.make_rotation_matrix(particle.theta - old_odom_xy_theta[2])
# rotate the motion vector, add the result to the particle
rotated_delta = np.dot(rotationmatrix, delta[:2])
linear_randomness = np.random.normal(1, 0.2)
angular_randomness = np.random.uniform(particle.turn_multiplier, 0.3)
particle.x += rotated_delta[0] * linear_randomness
particle.y += rotated_delta[1] * linear_randomness
particle.theta += delta[2] * angular_randomness
# Make sure the particle's angle doesn't wrap
particle.theta = angle_diff(particle.theta, 0)
def make_rotation_matrix(self, theta):
""" make_rotation_matrix returns a rotation matrix given angle theta
Args:
theta (number): the angle of rotation in radians CCW
Returns:
ndarray: a two by two rotation matrix
"""
sinTheta = np.sin(theta)
cosTheta = np.cos(theta)
return np.array([[cosTheta, -sinTheta],
[sinTheta, cosTheta]])
def map_calc_range(self, x, y, theta):
""" Difficulty Level 3: implement a ray tracing likelihood model... Let me know if you are interested """
# TODO: nothing unless you want to try this alternate likelihood model
pass
def lost_particles(self):
""" lost_particles predicts which paricles are "lost" using unsupervised outlier detection.
In this case, we choose to use Scikit Learn - OneClassSVM
Args:
Returns:
inliers = particles that are not lost
outlier = particles that are lost
"""
# First format training data
x = [p.x for p in self.particle_cloud]
y = [p.y for p in self.particle_cloud]
X_train = np.array(zip(x, y))
# Next make unsupervised outlier detection model
# We have chosen to use OneClassSVM
# Lower nu to detect fewer outliers
# Here, we use 1/2 of the lost probability : self.p_lost / 2.0
clf = OneClassSVM(nu=.3, kernel="rbf", gamma=0.1)
clf.fit(X_train)
# Predict inliers and outliers
y_pred_train = clf.predict(X_train)
# Create inlier and outlier particle lists
inliers = []
outliers = []
# Iterate through particles and predictions to populate lists
for p, pred in zip(self.particle_cloud, y_pred_train):
if pred == 1:
inliers.append(p)
elif pred == -1:
outliers.append(p)
return inliers, outliers
def resample_particles(self):
""" Resample the particles according to the new particle weights.
The weights stored with each particle should define the probability that a particular
particle is selected in the resampling step. You may want to make use of the given helper
function draw_random_sample.
"""
# TODO: Dynamically decide how many particles we need
# make sure the distribution is normalized
self.normalize_particles()
# Calculate inlaying and exploring particle sets
inliers, outliers = self.lost_particles()
desired_outliers = int(self.n_particles * self.p_lost)
desired_inliers = int(self.n_particles - desired_outliers)
# Calculate the average turn_multiplier of the inliers
mean_turn_multipler = np.mean([p.turn_multiplier for p in inliers])
print "Estimated turn multiplier:", mean_turn_multipler
# Recalculate inliers
probabilities = [p.w for p in self.particle_cloud]
new_inliers = self.draw_random_sample(self.particle_cloud, probabilities, desired_inliers)
# Recalculate outliers
# This keeps some number of outlying particles around unchanged, and spreads the rest randomly around the map.
if desired_outliers > min(len(outliers), self.outliers_to_keep):
outliers.sort(key=lambda p: p.w, reverse=True)
num_to_make = desired_outliers - min(len(outliers), self.outliers_to_keep)
new_outliers = outliers[:self.outliers_to_keep] + \
[Particle().generate_uniformly_on_map(self.occupancy_field.map) for _ in xrange(num_to_make)]
for p in new_outliers:
p.turn_multiplier = mean_turn_multipler
else:
new_outliers = outliers[:desired_outliers]
# Set all of the weights back to the same value. Concentration of particles now reflects weight.
new_particles = new_inliers + new_outliers
for p in new_particles:
p.w = 1.0
p.turn_multiplier = np.random.normal(p.turn_multiplier, 0.1)
self.normalize_particles()
self.particle_cloud = new_particles
@staticmethod
def laser_uncertainty_model(distErr):
"""
Computes the probability of the laser returning a point distance distErr from the wall.
Note that this uses an exponential distribution instead of anything reasonable for computational speed.
Args:
distErr (float): The distance between the point returned and the nearest
wall on the map (in meters)
Returns:
probability (float): A probability, in the range 0...1
"""
# TODO: make these into rosparams
k = 0.1 # meters of half-life of distance probability for main distribution
probMiss = 0.05 # Base probability that the laser scan is totally confused
distErr = abs(distErr)
return (1 / (1 + probMiss)) * (probMiss + 1 / (distErr / k + 1))
def update_particles_with_laser(self, msg):
""" Updates the particle weights in response to the scan contained in the msg
Args:
msg (LaserScan): incoming message
"""
# Transform to cartesian coordinates
scan_points = PointCloud()
scan_points.header = msg.header
for i, range in enumerate(msg.ranges):
if range == 0:
continue
# Calculate point in laser coordinate frame
angle = msg.angle_min + i * msg.angle_increment
x = range * np.cos(angle)
y = range * np.sin(angle)
scan_points.points.append(Point32(x=x, y=y))
# Transform into base_link coordinates
scan_points = self.tf_listener.transformPointCloud('base_link', scan_points)
# For each particle...
for particle in self.particle_cloud:
# Create a 3x3 matrix that transforms points from the origin to the particle
rotmatrix = np.matrix([[np.cos(particle.theta), -np.sin(particle.theta), 0],
[np.sin(particle.theta), np.cos(particle.theta), 0],
[0, 0, 1]])
transmatrix = np.matrix([[1, 0, particle.x],
[0, 1, particle.y],
[0, 0, 1]])
mat33 = np.dot(transmatrix, rotmatrix)
# Iterate through the points in the laser scan
probabilities = []
for point in scan_points.points:
# Move the point onto the particle
xy = np.dot(mat33, np.array([point.x, point.y, 1]))
# Figure out the probability of that point
distToWall = self.occupancy_field.get_closest_obstacle_distance(xy.item(0), xy.item(1))
if np.isnan(distToWall):
continue
probabilities.append(self.laser_uncertainty_model(distToWall))
# Combine those into probability of this scan given hypothesized location
# This is the bullshit thing Paul showed
# TODO: exponent should be a rosparam
totalProb = np.sum([p ** 3 for p in probabilities]) / len(probabilities)
# Update the particle's probability with new info
particle.w *= totalProb
# Normalize particles
self.normalize_particles()
@staticmethod
def weighted_values(values, probabilities, size):
""" Return a random sample of size elements from the set values with the specified probabilities
values: the values to sample from (numpy.ndarray)
probabilities: the probability of selecting each element in values (numpy.ndarray)
size: the number of samples
"""
bins = np.add.accumulate(probabilities)
return values[np.digitize(random_sample(size), bins)]
@staticmethod
def draw_random_sample(choices, probabilities, n):
""" Return a random sample of n elements from the set choices with the specified probabilities
Args:
choices: the values to sample from represented as a list
probabilities: the probability of selecting each element in choices represented as a list
n: the number of samples
Returns:
samples (List): A list of n elements, deep-copied from choices
"""
values = np.array(range(len(choices)))
probs = np.array(probabilities)
bins = np.add.accumulate(probs)
inds = values[np.digitize(random_sample(n), bins)]
samples = []
for i in inds:
samples.append(deepcopy(choices[int(i)]))
return samples
def update_initial_pose(self, msg):
""" Callback function to handle re-initializing the particle filter based on a pose estimate.
These pose estimates could be generated by another ROS Node or could come from the rviz GUI """
xy_theta = convert_pose_to_xy_and_theta(msg.pose.pose)
self.initialize_particle_cloud(xy_theta)
self.fix_map_to_odom_transform(msg)
def initialize_particle_cloud(self, xy_theta=None):
""" Initialize the particle cloud.
Arguments
xy_theta: a triple consisting of the mean x, y, and theta (yaw) to initialize the
particle cloud around. If this input is ommitted, the odometry will be used """
if xy_theta is None:
xy_theta = convert_pose_to_xy_and_theta(self.odom_pose.pose)
self.particle_cloud = []
linear_variance = 0.5 # meters
angular_variance = 4
xs = np.random.normal(xy_theta[0], linear_variance, size=self.n_particles)
ys = np.random.normal(xy_theta[1], linear_variance, size=self.n_particles)
thetas = np.random.vonmises(xy_theta[2], angular_variance, size=self.n_particles)
self.particle_cloud = [Particle(x=xs[i], y=ys[i], theta=thetas[i]) for i in xrange(self.n_particles)]
self.normalize_particles()
self.update_robot_pose()
def normalize_particles(self):
""" Make sure the particle weights define a valid distribution (i.e. sum to 1.0) """
total = sum([p.w for p in self.particle_cloud])
if total != 0:
for p in self.particle_cloud:
p.w /= total
# Plan: divide each by the sum of all
# TODO: implement this
def publish_particles(self, msg):
particles_conv = []
for p in self.particle_cloud:
particles_conv.append(p.as_pose())
# actually send the message so that we can view it in rviz
self.particle_pub.publish(PoseArray(header=Header(stamp=rospy.Time.now(),
frame_id=self.map_frame),
poses=particles_conv))
def scan_received(self, msg):
""" This is the default logic for what to do when processing scan data.
Feel free to modify this, however, I hope it will provide a good
guide. The input msg is an object of type sensor_msgs/LaserScan """
if not (self.initialized):
# wait for initialization to complete
return
if not (self.tf_listener.canTransform(self.base_frame, msg.header.frame_id, msg.header.stamp)):
# need to know how to transform the laser to the base frame
# this will be given by either Gazebo or neato_node
return
if not (self.tf_listener.canTransform(self.base_frame, self.odom_frame, msg.header.stamp)):
# need to know how to transform between base and odometric frames
# this will eventually be published by either Gazebo or neato_node
return
startTime = time.clock()
# calculate pose of laser relative ot the robot base
p = PoseStamped(header=Header(stamp=rospy.Time(0),
frame_id=msg.header.frame_id))
self.laser_pose = self.tf_listener.transformPose(self.base_frame, p)
# find out where the robot thinks it is based on its odometry
p = PoseStamped(header=Header(stamp=msg.header.stamp,
frame_id=self.base_frame),
pose=Pose())
self.odom_pose = self.tf_listener.transformPose(self.odom_frame, p)
# store the the odometry pose in a more convenient format (x,y,theta)
new_odom_xy_theta = convert_pose_to_xy_and_theta(self.odom_pose.pose)
if not (self.particle_cloud):
# now that we have all of the necessary transforms we can update the particle cloud
self.initialize_particle_cloud()
# cache the last odometric pose so we can only update our particle filter if we move more than self.d_thresh or self.a_thresh
self.current_odom_xy_theta = new_odom_xy_theta
# update our map to odom transform now that the particles are initialized
self.fix_map_to_odom_transform(msg)
elif (math.fabs(new_odom_xy_theta[0] - self.current_odom_xy_theta[0]) > self.d_thresh or
math.fabs(new_odom_xy_theta[1] - self.current_odom_xy_theta[1]) > self.d_thresh or
math.fabs(new_odom_xy_theta[2] - self.current_odom_xy_theta[2]) > self.a_thresh):
# we have moved far enough to do an update!
self.update_particles_with_odom(msg) # update based on odometry
self.update_particles_with_laser(msg) # update based on laser scan
self.update_robot_pose() # update robot's pose
self.resample_particles() # resample particles to focus on areas of high density
self.fix_map_to_odom_transform(msg) # update map to odom transform now that we have new particles
print "Calculation time: {}ms".format((time.clock() - startTime) * 1000)
# publish particles (so things like rviz can see them)
self.publish_particles(msg)
def fix_map_to_odom_transform(self, msg):
""" This method constantly updates the offset of the map and
odometry coordinate systems based on the latest results from
the localizer
TODO: if you want to learn a lot about tf, reimplement this... I can provide
you with some hints as to what is going on here. """
(translation, rotation) = convert_pose_inverse_transform(self.robot_pose)
p = PoseStamped(pose=convert_translation_rotation_to_pose(translation, rotation),
header=Header(stamp=msg.header.stamp, frame_id=self.base_frame))
self.tf_listener.waitForTransform(self.base_frame, self.odom_frame, msg.header.stamp, rospy.Duration(1.0))
self.odom_to_map = self.tf_listener.transformPose(self.odom_frame, p)
(self.translation, self.rotation) = convert_pose_inverse_transform(self.odom_to_map.pose)
def broadcast_last_transform(self):
""" Make sure that we are always broadcasting the last map
to odom transformation. This is necessary so things like
move_base can work properly. """
if not (hasattr(self, 'translation') and hasattr(self, 'rotation')):
return
self.tf_broadcaster.sendTransform(self.translation,
self.rotation,
rospy.get_rostime(),
self.odom_frame,
self.map_frame)
class Controller():
Manual = 0
Automatic = 1
TakeOff = 2
def __init__(self):
self.pubNav = rospy.Publisher('cmd_vel', Twist, queue_size=1)
self.listener = TransformListener()
rospy.Subscriber("joy", Joy, self._joyChanged)
rospy.Subscriber("cmd_vel_telop", Twist, self._cmdVelTelopChanged)
self.cmd_vel_telop = Twist()
#self.pidX = PID(20, 12, 0.0, -30, 30, "x")
#self.pidY = PID(-20, -12, 0.0, -30, 30, "y")
#self.pidZ = PID(15000, 3000.0, 1500.0, 10000, 60000, "z")
#self.pidYaw = PID(50.0, 0.0, 0.0, -200.0, 200.0, "yaw")
self.pidX = PID(20, 12.0, 0.2, -30, 30, "x")
self.pidY = PID(-20, -12.0, -0.2, -30, 30, "y")
#50000 800
self.pidZ = PID(15000, 3000.0, 1500.0, 10000, 57000, "z")
self.pidYaw = PID(50.0, 0.0, 0.0, -200.0, 200.0, "yaw")
self.state = Controller.Manual
self.targetZ = 1
self.targetX = 0.0
self.targetY = -1.0
self.des_angle = 90.0
self.lastZ = 0.0
self.power = 50000.0
self.pubVz = rospy.Publisher('vel_z', Float32, queue_size=1)
self.lastJoy = Joy()
def _cmdVelTelopChanged(self, data):
self.cmd_vel_telop = data
if self.state == Controller.Manual:
self.pubNav.publish(data)
def pidReset(self):
self.pidX.reset()
self.pidZ.reset()
self.pidZ.reset()
self.pidYaw.reset()
def _joyChanged(self, data):
if len(data.buttons) == len(self.lastJoy.buttons):
delta = np.array(data.buttons) - np.array(self.lastJoy.buttons)
print ("Buton ok")
#Button 1
if delta[0] == 1 and self.state != Controller.Automatic:
print("Automatic!")
#thrust = self.cmd_vel_telop.linear.z
#print(thrust)
self.pidReset()
self.pidZ.integral = self.power/self.pidZ.ki
self.lastZ = 0.0
#self.targetZ = 1
self.state = Controller.Automatic
#Button 2
if delta[1] == 1 and self.state != Controller.Manual:
print("Manual!")
self.pubNav.publish(self.cmd_vel_telop)
self.state = Controller.Manual
#Button 3
if delta[2] == 1:
self.state = Controller.TakeOff
print("TakeOff!")
#Button 5
if delta[4] == 1:
self.targetY = 0.0
self.des_angle = -90.0
#print(self.targetZ)
#self.power += 100.0
print(self.power)
#self.state = Controller.Automatic
#Button 6
if delta[5] == 1:
self.targetY = -1.0
self.des_angle = 90.0
#print(self.targetZ)
#self.power -= 100.0
print(self.power)
#self.state = Controller.Automatic
self.lastJoy = data
def run(self):
thrust = 0
print("jello")
while not rospy.is_shutdown():
if self.state == Controller.TakeOff:
t = self.listener.getLatestCommonTime("/mocap", "/Nano_Mark3")
if self.listener.canTransform("/mocap", "/Nano_Mark3", t):
position, quaternion = self.listener.lookupTransform("/mocap","/Nano_Mark3", t)
print(position[0],position[1],position[2])
if position[2] > 0.2 or thrust > 54000:
self.pidReset()
#self.pidZ.integral = thrust / self.pidZ.ki
#self.targetZ = 0.5
self.state = Controller.Automatic
thrust = 0
else:
thrust += 100
self.power = thrust
msg = self.cmd_vel_telop
msg.linear.z = thrust
self.pubNav.publish(msg)
if self.state == Controller.Automatic:
# transform target world coordinates into local coordinates
t = self.listener.getLatestCommonTime("/mocap","/Nano_Mark3")
seconds = rospy.get_time()
print(t)
if self.listener.canTransform("/mocap","/Nano_Mark3", t):
position, quaternion = self.listener.lookupTransform("/mocap","/Nano_Mark3",t)
#print(position[0],position[1],position[2])
euler = tf.transformations.euler_from_quaternion(quaternion)
#print(euler[2]*(180/math.pi))
msg = self.cmd_vel_telop
#print(self.power)
if self.lastZ == 0.0:
self.lastZ = position[2]
last_time = seconds;
else:
dh = self.targetZ - position[2]
v_z = (position[2]-self.lastZ)/((seconds-last_time))
#print(v_z)
self.pubVz.publish(v_z)
#por encima de goal
if dh<0.0:
self.power -=50
#if v_z>0.0:
# self.power -=50
#else:
#self.power +=50
#por debajo de goal
if dh>0.0:
if self.power > 57000.0:
self.power = 57000.0
else:
self.power += 50
#if v_z<0.0:
# self.power +=50
# else:
# self.power -=50
print(self.power)
msg.linear.z = self.power
#Descompostion of the x and y contributions following the Z-Rotation
x_prim = self.pidX.update(0.0, self.targetX-position[0])
y_prim = self.pidY.update(0.0,self.targetY-position[1])
msg.linear.x = x_prim*math.cos(euler[2]) - y_prim*math.sin(euler[2])
msg.linear.y = x_prim*math.sin(euler[2]) + y_prim*math.cos(euler[2])
#---old stuff---
#msg.linear.x = self.pidX.update(0.0, 0.0-position[0])
#msg.linear.y = self.pidY.update(0.0,-1.0-position[1])
#msg.linear.z = self.pidZ.update(position[2],1.0)
#z_prim = self.pidZ.update(0.0,self.targetZ-position[2])
#print(z_prim)
#if z_prim < self.power:
# msg.linear.z = self.power
#else:
# msg.linear.z = z_prim
#msg.linear.z = z_prim
#msg.linear.z = self.pidZ.update(0.0,1.0-position[2]) #self.pidZ.update(position[2], self.targetZ)
#msg.angular.z = self.pidYaw.update(0.0,self.des_angle + euler[2]*(180/math.pi))
print(msg.linear.x,msg.linear.y,msg.linear.z,msg.angular.z)
#print(euler[2])
#print(msg.angular.z)
self.pubNav.publish(msg)
rospy.sleep(0.01)
class ParticleFilter:
""" The class that represents a Particle Filter ROS Node
Attributes list:
initialized: a Boolean flag to communicate to other class methods that initializaiton is complete
base_frame: the name of the robot base coordinate frame (should be "base_link" for most robots)
map_frame: the name of the map coordinate frame (should be "map" in most cases)
odom_frame: the name of the odometry coordinate frame (should be "odom" in most cases)
scan_topic: the name of the scan topic to listen to (should be "scan" in most cases)
n_particles: the number of particles in the filter
d_thresh: the amount of linear movement before triggering a filter update
a_thresh: the amount of angular movement before triggering a filter update
laser_max_distance: the maximum distance to an obstacle we should use in a likelihood calculation
pose_listener: a subscriber that listens for new approximate pose estimates (i.e. generated through the rviz GUI)
particle_pub: a publisher for the particle cloud
laser_subscriber: listens for new scan data on topic self.scan_topic
tf_listener: listener for coordinate transforms
tf_broadcaster: broadcaster for coordinate transforms
particle_cloud: a list of particles representing a probability distribution over robot poses
current_odom_xy_theta: the pose of the robot in the odometry frame when the last filter update was performed.
The pose is expressed as a list [x,y,theta] (where theta is the yaw)
map: the map we will be localizing ourselves in. The map should be of type nav_msgs/OccupancyGrid
"""
def __init__(self):
self.initialized = False # make sure we don't perform updates before everything is setup
rospy.init_node('pf') # tell roscore that we are creating a new node named "pf"
self.base_frame = "base_link" # the frame of the robot base
self.map_frame = "map" # the name of the map coordinate frame
self.odom_frame = "odom" # the name of the odometry coordinate frame
self.scan_topic = "scan" # the topic where we will get laser scans from
self.n_particles = 300 # the number of particles to use
self.d_thresh = 0.2 # the amount of linear movement before performing an update
self.a_thresh = math.pi/6 # the amount of angular movement before performing an update
self.laser_max_distance = 2.0 # maximum penalty to assess in the likelihood field model
# TODO: define additional constants if needed
# Setup pubs and subs
# pose_listener responds to selection of a new approximate robot location (for instance using rviz)
self.pose_listener = rospy.Subscriber("initialpose", PoseWithCovarianceStamped, self.update_initial_pose)
# publish the current particle cloud. This enables viewing particles in rviz.
self.particle_pub = rospy.Publisher("particlecloud", PoseArray, queue_size=10)
# laser_subscriber listens for data from the lidar
self.laser_subscriber = rospy.Subscriber(self.scan_topic, LaserScan, self.scan_received)
# enable listening for and broadcasting coordinate transforms
self.tf_listener = TransformListener()
self.tf_broadcaster = TransformBroadcaster()
self.particle_cloud = []
self.current_odom_xy_theta = []
# request the map from the map server, the map should be of type nav_msgs/OccupancyGrid
# TODO: fill in the appropriate service call here. The resultant map should be assigned be passed
# into the init method for OccupancyField
gettingMap = rospy.ServiceProxy('static_map',GetMap)
myMap = gettingMap().map
# for now we have commented out the occupancy field initialization until you can successfully fetch the map
self.occupancy_field = OccupancyField(myMap)
self.initialized = True
def update_robot_pose(self):
""" Update the estimate of the robot's pose given the updated particles.
There are two logical methods for this:
(1): compute the mean pose
(2): compute the most likely pose (i.e. the mode of the distribution)
"""
# first make sure that the particle weights are normalized
self.normalize_particles()
# TODO: assign the lastest pose into self.robot_pose as a geometry_msgs.Pose object
# just to get started we will fix the robot's pose to always be at the origin
self.robot_pose = Pose()
best_particle = Particle(0,0,0,0)
for particle in self.particle_cloud:
if particle.w > best_particle.w:
best_particle = particle
self.robot_pose = best_particle.as_pose()
#print "updated pose" + str(self.robot_pose)
def update_particles_with_odom(self, msg):
""" Update the particles using the newly given odometry pose.
The function computes the value delta which is a tuple (x,y,theta)
that indicates the change in position and angle between the odometry
when the particles were last updated and the current odometry.
msg: this is not really needed to implement this, but is here just in case.
"""
new_odom_xy_theta = convert_pose_to_xy_and_theta(self.odom_pose.pose)
# compute the change in x,y,theta since our last update
if self.current_odom_xy_theta:
old_odom_xy_theta = self.current_odom_xy_theta
delta = (new_odom_xy_theta[0] - self.current_odom_xy_theta[0],
new_odom_xy_theta[1] - self.current_odom_xy_theta[1],
new_odom_xy_theta[2] - self.current_odom_xy_theta[2])
self.current_odom_xy_theta = new_odom_xy_theta
else:
self.current_odom_xy_theta = new_odom_xy_theta
return
for particle in self.particle_cloud:
psi = math.atan2(delta[1],delta[0])- old_odom_xy_theta[2]
r = math.sqrt((delta[0])**2 + (delta[1])**2)
particle.x=particle.x+ r*math.cos(old_odom_xy_theta[2]+psi)
particle.y = particle.y + r*math.sin(old_odom_xy_theta[2]+psi)
particle.theta = old_odom_xy_theta[2] + delta[2]
# TODO: modify particles using delta
# For added difficulty: Implement sample_motion_odometry (Prob Rob p 136)
def map_calc_range(self,x,y,theta):
""" Difficulty Level 3: implement a ray tracing likelihood model... Let me know if you are interested """
# TODO: nothing unless you want to try this alternate likelihood model
pass
def resample_particles(self):
""" Resample the particles according to the new particle weights.
The weights stored with each particle should define the probability that a particular
particle is selected in the resampling step. You may want to make use of the given helper
function draw_random_sample.
"""
# make sure the distribution is normalized
self.normalize_particles()
# TODO: fill out the rest of the implementation
new_particles = []
probabilities = []
for particle in self.particle_cloud:
probabilities.append(particle.w)
new_particles = ParticleFilter.draw_random_sample(self.particle_cloud, probabilities, len(self.particle_cloud))
self.particle_cloud = new_particles
print 'Particle cloud element 0' + '%s' %str(self.particle_cloud[0].x) + str(self.particle_cloud[0].y)
print 'Particle cloud element 1' + '%s' %str(self.particle_cloud[1].x) + str(self.particle_cloud[1].y)
def update_particles_with_laser(self, msg):
""" Updates the particle weights in response to the scan contained in the msg """
# TODO: implement this
#print str(msg.range[0])
#We ctually need to go through all of the range so a for loop form 0 to 360
#We also need to delete any ranges that return 0 because that is a false reading and causes problems
#print msg
for part in self.particle_cloud:
total_distace = 0
average_distance = 0
count = 0
for angle in range(359):
distance_in_front = msg.ranges[angle]
if distance_in_front == 0:
pass
else:
count +=1
rad = angle/360 * 2 * math.pi
part.x = part.x + distance_in_front*math.cos(part.theta + rad)
part.y = part.y + distance_in_front*math.sin(part.theta + rad)
distance = OccupancyField.get_closest_obstacle_distance(self.occupancy_field, part.x, part.y)
total_distace += distance
average_distance = total_distace/count
part.w=(math.e**((-average_distance)**2))
#pass
@staticmethod
def weighted_values(values, probabilities, size):
""" Return a random sample of size elements from the set values with the specified probabilities
values: the values to sample from (numpy.ndarray)
probabilities: the probability of selecting each element in values (numpy.ndarray)
size: the number of samples
"""
bins = np.add.accumulate(probabilities)
return values[np.digitize(random_sample(size), bins)]
@staticmethod
def draw_random_sample(choices, probabilities, n):
""" Return a random sample of n elements from the set choices with the specified probabilities
choices: the values to sample from represented as a list
probabilities: the probability of selecting each element in choices represented as a list
n: the number of samples
"""
values = np.array(range(len(choices)))
probs = np.array(probabilities)
bins = np.add.accumulate(probs)
inds = values[np.digitize(random_sample(n), bins)]
samples = []
for i in inds:
samples.append(deepcopy(choices[int(i)]))
return samples
def update_initial_pose(self, msg):
""" Callback function to handle re-initializing the particle filter based on a pose estimate.
These pose estimates could be generated by another ROS Node or could come from the rviz GUI """
xy_theta = convert_pose_to_xy_and_theta(msg.pose.pose)
self.initialize_particle_cloud(xy_theta)
self.fix_map_to_odom_transform(msg)
def initialize_particle_cloud(self, xy_theta=None):
""" Initialize the particle cloud.
Arguments
xy_theta: a triple consisting of the mean x, y, and theta (yaw) to initialize the
particle cloud around. If this input is ommitted, the odometry will be used """
if xy_theta == None:
xy_theta = convert_pose_to_xy_and_theta(self.odom_pose.pose)
self.particle_cloud = []
#one particle at origin
#self.particle_cloud.append(Particle(0,0,0))
# TODO create particles
for i in range(self.n_particles):
self.particle_cloud.append(Particle(randn(), randn(),randn())) #add robot location guess to each number
self.normalize_particles()
self.update_robot_pose()
#print self.particle_cloud
def normalize_particles(self):
""" Make sure the particle weights define a valid distribution (i.e. sum to 1.0) """
pass
# TODO: implement this
#add up all weights of all particles, divide each particle by this number
sumWeight = 0
for particle in self.particle_cloud:
sumWeight += particle.w
for particle in self.particle_cloud:
particle.w /=sumWeight
def publish_particles(self, msg):
particles_conv = []
for p in self.particle_cloud:
particles_conv.append(p.as_pose())
# actually send the message so that we can view it in rviz
self.particle_pub.publish(PoseArray(header=Header(stamp=rospy.Time.now(),
frame_id=self.map_frame),
poses=particles_conv))
def scan_received(self, msg):
""" This is the default logic for what to do when processing scan data.
Feel free to modify this, however, I hope it will provide a good
guide. The input msg is an object of type sensor_msgs/LaserScan """
if not(self.initialized):
# wait for initialization to complete
return
if not(self.tf_listener.canTransform(self.base_frame,msg.header.frame_id,msg.header.stamp)):
# need to know how to transform the laser to the base frame
# this will be given by either Gazebo or neato_node
return
if not(self.tf_listener.canTransform(self.base_frame,self.odom_frame,msg.header.stamp)):
# need to know how to transform between base and odometric frames
# this will eventually be published by either Gazebo or neato_node
return
# calculate pose of laser relative ot the robot base
p = PoseStamped(header=Header(stamp=rospy.Time(0),
frame_id=msg.header.frame_id))
self.laser_pose = self.tf_listener.transformPose(self.base_frame,p)
# find out where the robot thinks it is based on its odometry
p = PoseStamped(header=Header(stamp=msg.header.stamp,
frame_id=self.base_frame),
pose=Pose())
self.odom_pose = self.tf_listener.transformPose(self.odom_frame, p)
# store the the odometry pose in a more convenient format (x,y,theta)
new_odom_xy_theta = convert_pose_to_xy_and_theta(self.odom_pose.pose)
if not(self.particle_cloud):
# now that we have all of the necessary transforms we can update the particle cloud
self.initialize_particle_cloud()
# cache the last odometric pose so we can only update our particle filter if we move more than self.d_thresh or self.a_thresh
self.current_odom_xy_theta = new_odom_xy_theta
# update our map to odom transform now that the particles are initialized
self.fix_map_to_odom_transform(msg)
elif (math.fabs(new_odom_xy_theta[0] - self.current_odom_xy_theta[0]) > self.d_thresh or
math.fabs(new_odom_xy_theta[1] - self.current_odom_xy_theta[1]) > self.d_thresh or
math.fabs(new_odom_xy_theta[2] - self.current_odom_xy_theta[2]) > self.a_thresh):
# we have moved far enough to do an update!
self.update_particles_with_odom(msg) # update based on odometry
self.update_particles_with_laser(msg) # update based on laser scan
self.update_robot_pose() # update robot's pose
self.resample_particles() # resample particles to focus on areas of high density
self.fix_map_to_odom_transform(msg) # update map to odom transform now that we have new particles
# publish particles (so things like rviz can see them)
self.publish_particles(msg)
def fix_map_to_odom_transform(self, msg):
""" This method constantly updates the offset of the map and
odometry coordinate systems based on the latest results from
the localizer """
(translation, rotation) = convert_pose_inverse_transform(self.robot_pose)
p = PoseStamped(pose=convert_translation_rotation_to_pose(translation,rotation),
header=Header(stamp=msg.header.stamp,frame_id=self.base_frame))
self.odom_to_map = self.tf_listener.transformPose(self.odom_frame, p)
(self.translation, self.rotation) = convert_pose_inverse_transform(self.odom_to_map.pose)
def broadcast_last_transform(self):
""" Make sure that we are always broadcasting the last map
to odom transformation. This is necessary so things like
move_base can work properly. """
if not(hasattr(self,'translation') and hasattr(self,'rotation')):
return
self.tf_broadcaster.sendTransform(self.translation,
self.rotation,
rospy.get_rostime(),
self.odom_frame,
self.map_frame)
class ParticleFilter:
""" The class that represents a Particle Filter ROS Node
Attributes list:
initialized: a Boolean flag to communicate to other class methods that initializaiton is complete
base_frame: the name of the robot base coordinate frame (should be "base_link" for most robots)
map_frame: the name of the map coordinate frame (should be "map" in most cases)
odom_frame: the name of the odometry coordinate frame (should be "odom" in most cases)
scan_topic: the name of the scan topic to listen to (should be "scan" in most cases)
n_particles: the number of particles in the filter
d_thresh: the amount of linear movement before triggering a filter update
a_thresh: the amount of angular movement before triggering a filter update
laser_max_distance: the maximum distance to an obstacle we should use in a likelihood calculation
pose_listener: a subscriber that listens for new approximate pose estimates (i.e. generated through the rviz GUI)
particle_pub: a publisher for the particle cloud
laser_subscriber: listens for new scan data on topic self.scan_topic
tf_listener: listener for coordinate transforms
tf_broadcaster: broadcaster for coordinate transforms
particle_cloud: a list of particles representing a probability distribution over robot poses
current_odom_xy_theta: the pose of the robot in the odometry frame when the last filter update was performed.
The pose is expressed as a list [x,y,theta] (where theta is the yaw)
map: the map we will be localizing ourselves in. The map should be of type nav_msgs/OccupancyGrid
"""
def __init__(self):
self.initialized = False # make sure we don't perform updates before everything is setup
rospy.init_node('pf') # tell roscore that we are creating a new node named "pf"
self.base_frame = "base_link" # the frame of the robot base
self.map_frame = "map" # the name of the map coordinate frame
self.odom_frame = "odom" # the name of the odometry coordinate frame
self.scan_topic = "scan" # the topic where we will get laser scans from
self.n_particles = 500 # the number of particles to use
self.d_thresh = 0.2 # the amount of linear movement before performing an update
self.a_thresh = math.pi/6 # the amount of angular movement before performing an update
self.laser_max_distance = 2.0 # maximum penalty to assess in the likelihood field model
self.sigma = 0.1
# TODO: define additional constants if needed
# Setup pubs and subs
# pose_listener responds to selection of a new approximate robot location (for instance using rviz)
self.pose_listener = rospy.Subscriber("initialpose", PoseWithCovarianceStamped, self.update_initial_pose)
# publish the current particle cloud. This enables viewing particles in rviz.
self.particle_pub = rospy.Publisher("particlecloud", PoseArray, queue_size=10)
# laser_subscriber listens for data from the lidar
self.laser_subscriber = rospy.Subscriber(self.scan_topic, LaserScan, self.scan_received)
# enable listening for and broadcasting coordinate transforms
self.tf_listener = TransformListener()
self.tf_broadcaster = TransformBroadcaster()
self.particle_cloud = []
self.current_odom_xy_theta = []
rospy.wait_for_service("static_map")
static_map = rospy.ServiceProxy("static_map", GetMap)
try:
map = static_map().map
except:
print("Could not receive map")
# for now we have commented out the occupancy field initialization until you can successfully fetch the map
self.occupancy_field = OccupancyField(map)
self.initialized = True
def update_robot_pose(self):
""" Update the estimate of the robot's pose given the updated particles.
There are two logical methods for this:
(1): compute the mean pose
(2): compute the most likely pose (i.e. the mode of the distribution)
"""
# first make sure that the particle weights are normalized
self.normalize_particles()
mean_x = 0
mean_y = 0
mean_theta = 0
mean_x_vector = 0
mean_y_vector = 0
for p in self.particle_cloud:
mean_x += p.x*p.w
mean_y += p.y*p.w
mean_x_vector += math.cos(p.theta)*p.w
mean_y_vector += math.sin(p.theta)*p.w
mean_theta = math.atan2(mean_y_vector, mean_x_vector)
self.robot_pose = Particle(x=mean_x,y=mean_y,theta=mean_theta).as_pose()
def update_particles_with_odom(self, msg):
""" Update the particles using the newly given odometry pose.
The function computes the value delta which is a tuple (x,y,theta)
that indicates the change in position and angle between the odometry
when the particles were last updated and the current odometry.
msg: this is not really needed to implement this, but is here just in case.
"""
new_odom_xy_theta = convert_pose_to_xy_and_theta(self.odom_pose.pose)
# compute the change in x,y,theta since our last update
if self.current_odom_xy_theta:
old_odom_xy_theta = self.current_odom_xy_theta
delta = {'x': new_odom_xy_theta[0] - self.current_odom_xy_theta[0],
'y': new_odom_xy_theta[1] - self.current_odom_xy_theta[1],
'theta': new_odom_xy_theta[2] - self.current_odom_xy_theta[2]}
delta['r'] = math.sqrt(delta['x']**2 + delta['y']**2)
delta['rot'] = angle_diff(math.atan2(delta['y'],delta['x']), old_odom_xy_theta[2])
self.current_odom_xy_theta = new_odom_xy_theta
else:
self.current_odom_xy_theta = new_odom_xy_theta
return
for p in self.particle_cloud:
p.x += delta['r']*math.cos(delta['rot'] + p.theta)
p.y += delta['r']*math.sin(delta['rot'] + p.theta)
p.theta += delta['theta']
def map_calc_range(self,x,y,theta):
""" Difficulty Level 3: implement a ray tracing likelihood model... Let me know if you are interested """
# TODO(NOPE): nothing unless you want to try this alternate likelihood model
pass
def resample_particles(self):
""" Resample the particles according to the new particle weights.
The weights stored with each particle should define the probability that a particular
particle is selected in the resampling step. You may want to make use of the given helper
function draw_random_sample.
"""
# make sure the distribution is normalized
self.normalize_particles()
indices = [i for i in range(len(self.particle_cloud))]
probs = [p.w for p in self.particle_cloud]
# print('b')
# print(probs)
new_indices = self.draw_random_sample(choices=indices, probabilities=probs, n=(self.n_particles))
new_particles = []
for i in new_indices:
clean_index = int(i)
old_particle = self.particle_cloud[clean_index]
new_particles.append(Particle(x=old_particle.x+gauss(0,.05),y=old_particle.y+gauss(0,.05),theta=old_particle.theta+gauss(0,.05)))
self.particle_cloud = new_particles
self.normalize_particles()
def update_particles_with_laser(self, msg):
""" Updates the particle weights in response to the scan contained in the msg """
for p in self.particle_cloud:
weight_sum = 0
for i in range(360):
n_o = p.nearest_obstacle(i, msg.ranges[i])
error = self.occupancy_field.get_closest_obstacle_distance(n_o[0], n_o[1])
weight_sum += math.exp(-error*error/(2*self.sigma**2))
p.w = weight_sum / 360
# print(p.w)
self.normalize_particles()
@staticmethod
def weighted_values(values, probabilities, size):
""" Return a random sample of size elements from the set values with the specified probabilities
values: the values to sample from (numpy.ndarray)
probabilities: the probability of selecting each element in values (numpy.ndarray)
size: the number of samples
"""
bins = np.add.accumulate(probabilities)
return values[np.digitize(random_sample(size), bins)]
@staticmethod
def draw_random_sample(choices, probabilities, n):
""" Return a random sample of n elements from the set choices with the specified probabilities
choices: the values to sample from represented as a list
probabilities: the probability of selecting each element in choices represented as a list
n: the number of samples
"""
values = np.array(range(len(choices)))
probs = np.array(probabilities)
bins = np.add.accumulate(probs)
inds = values[np.digitize(random_sample(n), bins)]
samples = []
for i in inds:
samples.append(deepcopy(choices[int(i)]))
return samples
def update_initial_pose(self, msg):
""" Callback function to handle re-initializing the particle filter based on a pose estimate.
These pose estimates could be generated by another ROS Node or could come from the rviz GUI """
xy_theta = convert_pose_to_xy_and_theta(msg.pose.pose)
self.initialize_particle_cloud(xy_theta)
self.fix_map_to_odom_transform(msg)
def initialize_particle_cloud(self, xy_theta=None):
""" Initialize the particle cloud.
Arguments
xy_theta: a triple consisting of the mean x, y, and theta (yaw) to initialize the particle cloud around. If this input is ommitted, the odometry will be used """
if xy_theta == None:
xy_theta = convert_pose_to_xy_and_theta(self.odom_pose.pose)
self.particle_cloud = []
for i in range(self.n_particles):
self.particle_cloud.append(Particle(x=xy_theta[0]+gauss(0,0.25),y=xy_theta[1]+gauss(0,0.25),theta=xy_theta[2]+gauss(0,0.25)))
self.normalize_particles()
self.update_robot_pose()
def normalize_particles(self):
""" Make sure the particle weights define a valid distribution (i.e. sum to 1.0) """
w = [deepcopy(p.w) for p in self.particle_cloud]
z = sum(w)
print(z)
if z > 0:
for i in range(len(self.particle_cloud)):
self.particle_cloud[i].w = w[i] / z
else:
for i in range(len(self.particle_cloud)):
self.particle_cloud[i].w = 1/len(self.particle_cloud)
print(sum([p.w for p in self.particle_cloud]))
def publish_particles(self, msg):
particles_conv = []
for p in self.particle_cloud:
particles_conv.append(p.as_pose())
# actually send the message so that we can view it in rviz
self.particle_pub.publish(PoseArray(header=Header(stamp=rospy.Time.now(),
frame_id=self.map_frame),
poses=particles_conv))
def scan_received(self, msg):
""" This is the default logic for what to do when processing scan data.
Feel free to modify this, however, I hope it will provide a good
guide. The input msg is an object of type sensor_msgs/LaserScan """
if not(self.initialized):
# wait for initialization to complete
return
if not(self.tf_listener.canTransform(self.base_frame,msg.header.frame_id,msg.header.stamp)):
# need to know how to transform the laser to the base frame
# this will be given by either Gazebo or neato_node
return
if not(self.tf_listener.canTransform(self.base_frame,self.odom_frame,msg.header.stamp)):
# need to know how to transform between base and odometric frames
# this will eventually be published by either Gazebo or neato_node
return
# calculate pose of laser relative ot the robot base
p = PoseStamped(header=Header(stamp=rospy.Time(0),
frame_id=msg.header.frame_id))
self.laser_pose = self.tf_listener.transformPose(self.base_frame,p)
# find out where the robot thinks it is based on its odometry
p = PoseStamped(header=Header(stamp=msg.header.stamp,
frame_id=self.base_frame),
pose=Pose())
self.odom_pose = self.tf_listener.transformPose(self.odom_frame, p)
# store the the odometry pose in a more convenient format (x,y,theta)
new_odom_xy_theta = convert_pose_to_xy_and_theta(self.odom_pose.pose)
if not(self.particle_cloud):
# now that we have all of the necessary transforms we can update the particle cloud
self.initialize_particle_cloud()
# cache the last odometric pose so we can only update our particle filter if we move more than self.d_thresh or self.a_thresh
self.current_odom_xy_theta = new_odom_xy_theta
# update our map to odom transform now that the particles are initialized
self.fix_map_to_odom_transform(msg)
elif (math.fabs(new_odom_xy_theta[0] - self.current_odom_xy_theta[0]) > self.d_thresh or
math.fabs(new_odom_xy_theta[1] - self.current_odom_xy_theta[1]) > self.d_thresh or
math.fabs(new_odom_xy_theta[2] - self.current_odom_xy_theta[2]) > self.a_thresh):
# we have moved far enough to do an update!
self.update_particles_with_odom(msg) # update based on odometry
self.update_particles_with_laser(msg) # update based on laser scan
self.update_robot_pose() # update robot's pose
self.resample_particles() # resample particles to focus on areas of high density
self.fix_map_to_odom_transform(msg) # update map to odom transform now that we have new particles
# publish particles (so things like rviz can see them)
self.publish_particles(msg)
def fix_map_to_odom_transform(self, msg):
""" This method constantly updates the offset of the map and
odometry coordinate systems based on the latest results from
the localizer """
(translation, rotation) = convert_pose_inverse_transform(self.robot_pose)
p = PoseStamped(pose=convert_translation_rotation_to_pose(translation,rotation),
header=Header(stamp=msg.header.stamp,frame_id=self.base_frame))
self.odom_to_map = self.tf_listener.transformPose(self.odom_frame, p)
(self.translation, self.rotation) = convert_pose_inverse_transform(self.odom_to_map.pose)
def broadcast_last_transform(self):
""" Make sure that we are always broadcasting the last map
to odom transformation. This is necessary so things like
move_base can work properly. """
if not(hasattr(self,'translation') and hasattr(self,'rotation')):
return
self.tf_broadcaster.sendTransform(self.translation,
self.rotation,
rospy.get_rostime(),
self.odom_frame,
self.map_frame)
class Controller():
Manual = 0
Automatic = 1
TakeOff = 2
Land = 3
def __init__(self):
self.pubNav = rospy.Publisher('cmd_vel', Twist, queue_size=1)
self.listener = TransformListener()
rospy.Subscriber("joy", Joy, self._joyChanged)
rospy.Subscriber("cmd_vel_telop", Twist, self._cmdVelTelopChanged)
self.cmd_vel_telop = Twist()
#self.pidX = PID(20, 12, 0.0, -30, 30, "x")
#self.pidY = PID(-20, -12, 0.0, -30, 30, "y")
#self.pidZ = PID(15000, 3000.0, 1500.0, 10000, 60000, "z")
#self.pidYaw = PID(50.0, 0.0, 0.0, -200.0, 200.0, "yaw")
self.pidX = PID(20, 12, 0.0, -20, 20, "x")
self.pidY = PID(-20, -12, 0.0, -20, 20, "y")
#50000 800
self.pidZ = PID(15000, 3000.0, 1500.0, 10000, 60000, "z")
self.pidYaw = PID(50.0, 0.0, 0.0, -100.0, 100.0, "yaw")
self.state = Controller.Manual
#Target Values
self.pubtarX = rospy.Publisher('target_x', Float32, queue_size=1)
self.pubtarY = rospy.Publisher('target_y', Float32, queue_size=1)
self.pubtarZ = rospy.Publisher('target_z', Float32, queue_size=1)
self.targetX = 0.0
self.targetY = 0.0
self.targetZ = 0.5
self.des_angle = 0.0
#self.power = 50000.0
#Actual Values
self.pubrealX = rospy.Publisher('real_x', Float32, queue_size=1)
self.pubrealY = rospy.Publisher('real_y', Float32, queue_size=1)
self.pubrealZ = rospy.Publisher('real_z', Float32, queue_size=1)
self.lastJoy = Joy()
#Path view
self.pubPath = rospy.Publisher('cf_Uni_path', MarkerArray, queue_size=100)
self.path = MarkerArray()
#self.p = []
#Square trajectory
self.square_start = False
self.square_pos = 0
#self.square =[[0.5,0.5,0.5,0.0],
# [0.5,-0.5,0.5,90.0],
# [-0.5,-0.5,0.5,180.0],
# [-0.5,0.5,0.5,270.0]]
#landing flag
self.land_flag = False
self.power = 0.0
def _cmdVelTelopChanged(self, data):
self.cmd_vel_telop = data
if self.state == Controller.Manual:
self.pubNav.publish(data)
def pidReset(self):
self.pidX.reset()
self.pidZ.reset()
self.pidZ.reset()
self.pidYaw.reset()
def square_go(self):
if self.square_start == False:
self.square_pos = 0
self.targetX = square[self.square_pos][0]
self.targetY = square[self.square_pos][1]
self.targetZ = square[self.square_pos][2]
self.des_angle = square[self.square_pos][3]
self.square_pos = self.square_pos + 1
self.square_start = True
else:
self.targetX = square[self.square_pos][0]
self.targetY = square[self.square_pos][1]
self.targetZ = square[self.square_pos][2]
self.des_angle = square[self.square_pos][3]
self.square_pos = self.square_pos + 1
if self.square_pos == 4:
self.square_pos = 0
def _joyChanged(self, data):
if len(data.buttons) == len(self.lastJoy.buttons):
delta = np.array(data.buttons) - np.array(self.lastJoy.buttons)
print ("Buton ok")
#Button 1
if delta[0] == 1 and self.state != Controller.Automatic:
print("Automatic!")
self.land_flag = False
#thrust = self.cmd_vel_telop.linear.z
#print(thrust)
self.pidReset()
self.pidZ.integral = 40.0
#self.targetZ = 1
self.state = Controller.Automatic
#Button 2
if delta[1] == 1 and self.state != Controller.Manual:
print("Manual!")
self.land_flag = False
self.pubNav.publish(self.cmd_vel_telop)
self.state = Controller.Manual
#Button 3
if delta[2] == 1:
self.land_flag = False
self.state = Controller.TakeOff
print("TakeOff!")
#Button 4
if delta[3] == 1:
self.land_flag = True
print("Landing!")
self.square_start = False
self.targetX = 0.0
self.targetY = 0.0
self.targetZ = 0.4
self.des_angle = 0.0
self.state = Controller.Automatic
#Button 5
if delta[4] == 1:
self.square_go()
#self.targetX = square[0][0]
#self.targetY = square[0][1]
#self.targetZ = square[0][2]
#self.des_angle = square[0][3]
#print(self.targetZ)
#self.power += 100.0
#print(self.power)
self.state = Controller.Automatic
#Button 6
if delta[5] == 1:
self.square_start = False
self.targetX = 0.0
self.targetY = 0.0
self.targetZ = 0.5
self.des_angle = 0.0
#print(self.targetZ)
#self.power -= 100.0
#print(self.power)
self.state = Controller.Automatic
self.lastJoy = data
def run(self):
thrust = 0
print("jello")
while not rospy.is_shutdown():
if self.state == Controller.TakeOff:
t = self.listener.getLatestCommonTime("/mocap", "/Nano_Mark_Gon4")
print(t,self.listener.canTransform("/mocap", "/Nano_Mark_Gon4", t))
if self.listener.canTransform("/mocap", "/Nano_Mark_Gon4", t):
position, quaternion = self.listener.lookupTransform("/mocap","/Nano_Mark_Gon4", t)
print(position[0],position[1],position[2])
#if position[2] > 2.0 or thrust > 54000:
if thrust > 55000:
self.pidReset()
self.pidZ.integral = thrust / self.pidZ.ki
#self.targetZ = 0.5
self.state = Controller.Automatic
thrust = 0
else:
thrust += 500
#self.power = thrust
msg = self.cmd_vel_telop
msg.linear.z = thrust
self.pubNav.publish(msg)
if self.state == Controller.Land:
t = self.listener.getLatestCommonTime("/mocap", "/Nano_Mark_Gon4")
if self.listener.canTransform("/mocap", "/Nano_Mark_Gon4", t):
position, quaternion = self.listener.lookupTransform("/mocap","/Nano_Mark_Gon4", t)
if position[2] > 0.05:
msg_land = self.cmd_vel_telop
self.power -=100
msg_land.linear.z = self.power
self.pubNav.publish(msg_land)
else:
msg_land = self.cmd_vel_telop
msg_land.linear.z = 0
self.pubNav.publish(msg_land)
if self.state == Controller.Automatic:
# transform target world coordinates into local coordinates
t = self.listener.getLatestCommonTime("/mocap","/Nano_Mark_Gon4")
print(t)
if self.listener.canTransform("/mocap","/Nano_Mark_Gon4", t):
position, quaternion = self.listener.lookupTransform("/mocap","/Nano_Mark_Gon4",t)
#print(position[0],position[1],position[2])
euler = tf.transformations.euler_from_quaternion(quaternion)
print(euler[2]*(180/math.pi))
msg = self.cmd_vel_telop
#print(self.power)
#Descompostion of the x and y contributions following the Z-Rotation
x_prim = self.pidX.update(0.0, self.targetX-position[0])
y_prim = self.pidY.update(0.0,self.targetY-position[1])
msg.linear.x = x_prim*math.cos(euler[2]) - y_prim*math.sin(euler[2])
msg.linear.y = x_prim*math.sin(euler[2]) + y_prim*math.cos(euler[2])
#---old stuff---
#msg.linear.x = self.pidX.update(0.0, 0.0-position[0])
#msg.linear.y = self.pidY.update(0.0,-1.0-position[1])
#msg.linear.z = self.pidZ.update(position[2],1.0)
#z_prim = self.pidZ.update(position[2],self.targetZ)
#print(z_prim)
#if z_prim < self.power:
# msg.linear.z = self.power
#else:
# msg.linear.z = z_prim
#msg.linear.z = self.power
#print(self.power)
msg.linear.z = self.pidZ.update(0.0,self.targetZ-position[2]) #self.pidZ.update(position[2], self.targetZ)
msg.angular.z = self.pidYaw.update(0.0,self.des_angle*(math.pi/180) + euler[2])#*(180/math.pi))
#msg.angular.z = self.pidYaw.update(0.0,self.des_angle - euler[2])#*(180/math.pi))
print(msg.linear.x,msg.linear.y,msg.linear.z,msg.angular.z)
#print(euler[2])
#print(msg.angular.z)
self.pubNav.publish(msg)
#Publish Real and Target position
self.pubtarX.publish(self.targetX)
self.pubtarY.publish(self.targetY)
self.pubtarZ.publish(self.targetZ)
self.pubrealX.publish(position[0])
self.pubrealY.publish(position[1])
self.pubrealZ.publish(position[2])
#change square point
if abs(self.targetX-position[0])<0.08 and \
abs(self.targetY-position[1])<0.08 and \
abs(self.targetZ-position[2])<0.08 and \
self.square_start == True:
self.square_go()
#Landing
if abs(self.targetX-position[0])<0.1 and \
abs(self.targetY-position[1])<0.1 and \
abs(self.targetZ-position[2])<0.1 and \
self.land_flag == True:
self.state = Controller.Land
self.power = msg.linear.z
#Publish Path
#point = Marker()
#line = Marker()
#point.header.frame_id = line.header.frame_id = 'mocap'
#POINTS
#point.action = point.ADD
#point.pose.orientation.w = 1.0
#point.id = 0
#point.type = point.POINTS
#point.scale.x = 0.01
#point.scale.y = 0.01
#point.color.g = 1.0
#point.color.a = 1.0
#LINE
#line.action = line.ADD
#line.pose.orientation.w = 1.0
#line.id = 1
#line.type = line.LINE_STRIP
#line.scale.x = 0.01
#line.color.g = 1.0
#line.color.a = 1.0
#p = Point()
#p.x = position[0]
#p.y = position[1]
#p.z = position[2]
#point.points.append(p)
# line.points.append(p)
#self.path.markers.append(p)
#id = 0
#for m in self.path.markers:
# m.id = id
# id += 1
#self.pubPath.publish(self.path)
#self.pubPath.publish(point)
#self.pubPath.publish(line)
point = Marker()
point.header.frame_id = 'mocap'
point.type = point.SPHERE
#points.header.stamp = rospy.Time.now()
point.ns = 'cf_Uni_path'
point.action = point.ADD
#points.id = 0;
point.scale.x = 0.005
point.scale.y = 0.005
point.scale.z = 0.005
point.color.a = 1.0
point.color.r = 1.0
point.color.g = 1.0
point.color.b = 0.0
point.pose.orientation.w = 1.0
point.pose.position.x = position[0]
point.pose.position.y = position[1]
point.pose.position.z = position[2]
self.path.markers.append(point)
id = 0
for m in self.path.markers:
m.id = id
id += 1
self.pubPath.publish(self.path)
#point = Point()
#point.x = position[0]
#point.y = position[1]
#point.z = position[2]
#points.points.append(point)
#self.p.append(pos2path)
#self.path.header.stamp = rospy.Time.now()
#self.path.header.frame_id = 'mocap'
#self.path.poses = self.p
#self.pubPath.publish(points)
rospy.sleep(0.01)
class ParticleFilter:
""" The class that represents a Particle Filter ROS Node
Attributes list:
initialized: a Boolean flag to communicate to other class methods that initializaiton is complete
base_frame: the name of the robot base coordinate frame (should be "base_link" for most robots)
map_frame: the name of the map coordinate frame (should be "map" in most cases)
odom_frame: the name of the odometry coordinate frame (should be "odom" in most cases)
scan_topic: the name of the scan topic to listen to (should be "scan" in most cases)
n_particles: the number of particles in the filter
d_thresh: the amount of linear movement before triggering a filter update
a_thresh: the amount of angular movement before triggering a filter update
laser_max_distance: the maximum distance to an obstacle we should use in a likelihood calculation
pose_listener: a subscriber that listens for new approximate pose estimates (i.e. generated through the rviz GUI)
particle_pub: a publisher for the particle cloud
laser_subscriber: listens for new scan data on topic self.scan_topic
tf_listener: listener for coordinate transforms
tf_broadcaster: broadcaster for coordinate transforms
particle_cloud: a list of particles representing a probability distribution over robot poses
current_odom_xy_theta: the pose of the robot in the odometry frame when the last filter update was performed.
The pose is expressed as a list [x,y,theta] (where theta is the yaw)
map: the map we will be localizing ourselves in. The map should be of type nav_msgs/OccupancyGrid
robot_pose: estimated position of the robot of type geometry_msgs/Pose
"""
# some constants! :) -emily and franz
TAU = math.pi * 2.0
# to be used in update_particles_with_odom
RADIAL_SIGMA = .03 # meters
ORIENTATION_SIGMA = 0.03 * TAU
def __init__(self):
self.initialized = False # make sure we don't perform updates before everything is setup
rospy.init_node('pf') # tell roscore that we are creating a new node named "pf"
self.base_frame = "base_link" # the frame of the robot base
self.map_frame = "map" # the name of the map coordinate frame
self.odom_frame = "odom" # the name of the odometry coordinate frame
self.scan_topic = "scan" # the topic where we will get laser scans from
self.n_particles = 30 # the number of particles to use
self.d_thresh = 0.2 # the amount of linear movement before performing an update
self.a_thresh = math.pi / 6 # the amount of angular movement before performing an update
self.laser_max_distance = 2.0 # maximum penalty to assess in the likelihood field model
# TODO: define additional constants if needed
#set self.visualize_weights to True if you want to see a plot of xpos vs weights every time the particles are updated
self.visualize_weights = True
# Setup pubs and subs
# pose_listener responds to selection of a new approximate robot location (for instance using rviz)
self.pose_listener = rospy.Subscriber("initialpose", PoseWithCovarianceStamped, self.update_initial_pose)
# publish the current particle cloud. This enables viewing particles in rviz.
self.rawcloud_pub = rospy.Publisher("rawcloud", PoseArray, queue_size=1)
self.odomcloud_pub = rospy.Publisher("odomcloud", PoseArray, queue_size=1)
self.lasercloud_pub = rospy.Publisher("lasercloud", PoseArray, queue_size=1)
self.resamplecloud_pub = rospy.Publisher("resamplecloud", PoseArray, queue_size=1)
self.finalcloud_pub = rospy.Publisher("finalcloud", PoseArray, queue_size=1)
# laser_subscriber listens for data from the lidar
self.laser_subscriber = rospy.Subscriber(self.scan_topic, LaserScan, self.scan_received)
# enable listening for and broadcasting coordinate transforms
self.tf_listener = TransformListener()
self.tf_broadcaster = TransformBroadcaster()
self.particle_cloud = []
self.current_odom_xy_theta = []
# request the map from the map server, the map should be of type nav_msgs/OccupancyGrid
get_static_map = rospy.ServiceProxy('static_map', GetMap)
self.occupancy_field = OccupancyField(get_static_map().map)
self.robot_pose = Pose()
self.initialized = True
def update_robot_pose(self):
""" Update the estimate of the robot's pose given the updated particles.
There are two logical methods for this:
(1): compute the mean pose (level 2)
(2): compute the most likely pose (i.e. the mode of the distribution) (level 1)
"""
# first make sure that the particle weights are normalized
self.normalize_particles()
# compute mean pose by calculating the weighted average of each position and angle
mean_x = 0
mean_y = 0
mean_theta = 0
for particle in self.particle_cloud:
mean_x += particle.w * particle.x
mean_y += particle.w * particle.y
mean_theta += particle.w * particle.theta
mean_particle = Particle(mean_x, mean_y, mean_theta)
self.robot_pose = mean_particle.as_pose()
def update_particles_with_odom(self, msg):
""" Implement a simple version of this (Level 1) or a more complex one (Level 2) """
new_odom_xy_theta = TransformHelpers.convert_pose_to_xy_and_theta(self.odom_pose.pose)
# compute the change in x,y,theta since our last update
if self.current_odom_xy_theta:
old_odom_xy_theta = self.current_odom_xy_theta
delta = (
new_odom_xy_theta[0] - self.current_odom_xy_theta[0], new_odom_xy_theta[1] - self.current_odom_xy_theta[1],
new_odom_xy_theta[2] - self.current_odom_xy_theta[2])
self.current_odom_xy_theta = new_odom_xy_theta
else:
self.current_odom_xy_theta = new_odom_xy_theta
return
r1 = math.atan2(delta[1], delta[0]) - old_odom_xy_theta[2]
delta_distance = np.linalg.norm([delta[0], delta[1]])
r2 = delta[2] - r1
for particle in self.particle_cloud:
# randomly pick the deltas for radial distance, mean angle, and orientation angle
delta_random_radius = np.random.normal(0, ParticleFilter.RADIAL_SIGMA)
delta_random_mean_angle = random_sample() * ParticleFilter.TAU / 2.0
delta_random_orient_angle = np.random.normal(0, ParticleFilter.ORIENTATION_SIGMA)
# calculate the deltas
delta_random_x = delta_random_radius * math.cos(delta_random_mean_angle)
delta_random_y = delta_random_radius * math.sin(delta_random_mean_angle)
# update the mean (add deltas)
particle.theta += r1
particle.x += math.cos(particle.theta) * delta_distance + delta_random_x
particle.y += math.sin(particle.theta) * delta_distance + delta_random_y
particle.theta += r2 + delta_random_orient_angle
# For added difficulty: Implement sample_motion_odometry (Prob Rob p 136)
def map_calc_range(self, x, y, theta):
""" Difficulty Level 3: implement a ray tracing likelihood model... Let me know if you are interested """
# TODO: nothing unless you want to try this alternate likelihood model
pass
def resample_particles(self):
""" Resample the particles according to the new particle weights """
# make sure the distribution is normalized
self.normalize_particles()
probabilities = [particle.w for particle in self.particle_cloud]
new_particle_cloud = []
for i in range(self.n_particles):
random_particle = deepcopy(np.random.choice(self.particle_cloud, p=probabilities))
new_particle_cloud.append(random_particle)
self.particle_cloud = new_particle_cloud
def update_particles_with_laser(self, msg):
""" Updates the particle weights in response to the scan contained in the msg """
# compare the distance to the closest occupied location
# of the hypothesis and laser scan measurement
# give it a weight inversely proportional to the error
valid_ranges = self.filter_laser(msg.ranges)
for particle in self.particle_cloud:
total_probability_density = 1
for angle in valid_ranges:
radius = valid_ranges[angle]
angle = (angle+.25*ParticleFilter.TAU) % ParticleFilter.TAU
x = math.cos(angle+particle.theta) * radius + particle.x
y = math.sin(angle+particle.theta) * radius + particle.y
dist_to_nearest_neighbor = self.occupancy_field.get_closest_obstacle_distance(x, y)
# calculate probability of nearest neighbor's distance
probability_density = normal(dist_to_nearest_neighbor, .05)
total_probability_density *= 1 + probability_density #the 1+ is hacky
# TODO: make the total_probability_density function more legit
particle.w = total_probability_density
# rospy.loginfo(particle.w)
def visualize_p_weights(self):
""" Produces a plot of particle weights vs. x position """
# close any figures that are open
plt.close('all')
# initialize the things
xpos = np.zeros(len(self.particle_cloud))
weights = np.zeros(len(self.particle_cloud))
x_i = 0
weights_i = 0
# grab the current values
for p in self.particle_cloud:
xpos[x_i] = p.x
weights[weights_i] = p.w
x_i += 1
weights_i += 1
# plotting current xpos and weights
fig = plt.figure()
plt.xlabel('xpos')
plt.ylabel('weights')
plt.title('xpos vs weights')
plt.plot(xpos, weights, 'ro')
plt.show(block=False)
@staticmethod
def angle_normalize(z):
""" convenience function to map an angle to the range [-pi,pi] """
return math.atan2(math.sin(z), math.cos(z))
@staticmethod
def angle_diff(a, b):
""" Calculates the difference between angle a and angle b (both should be in radians)
the difference is always based on the closest rotation from angle a to angle b
examples:
angle_diff(.1,.2) -> -.1
angle_diff(.1, 2*math.pi - .1) -> .2
angle_diff(.1, .2+2*math.pi) -> -.1
"""
a = ParticleFilter.angle_normalize(a)
b = ParticleFilter.angle_normalize(b)
d1 = a - b
d2 = 2 * math.pi - math.fabs(d1)
if d1 > 0:
d2 *= -1.0
if math.fabs(d1) < math.fabs(d2):
return d1
else:
return d2
@staticmethod
def weighted_values(values, probabilities, size):
""" Return a random sample of size elements form the set values with the specified probabilities
values: the values to sample from (numpy.ndarray)
probabilities: the probability of selecting each element in values (numpy.ndarray)
size: the number of samples
"""
bins = np.add.accumulate(probabilities)
return values[np.digitize(random_sample(size), bins)]
def update_initial_pose(self, msg):
""" Callback function to handle re-initializing the particle filter based on a pose estimate.
These pose estimates could be generated by another ROS Node or could come from the rviz GUI """
xy_theta = TransformHelpers.convert_pose_to_xy_and_theta(msg.pose.pose)
self.initialize_particle_cloud(xy_theta)
self.fix_map_to_odom_transform(msg)
def initialize_particle_cloud(self):
""" Initialize the particle cloud.
Arguments
"""
rospy.loginfo("initialize particle cloud")
self.particle_cloud = []
map_info = self.occupancy_field.map.info
for i in range(self.n_particles):
x = random_sample()* map_info.width * map_info.resolution * 0.1
if random_sample() > 0.5:
x = -x
y = random_sample()* map_info.height * map_info.resolution * 0.1
if random_sample() > 0.5:
y = -y
theta = random_sample() * math.pi*2
self.particle_cloud.append(Particle(x, y, theta))
self.normalize_particles()
self.update_robot_pose()
def normalize_particles(self):
""" Make sure the particle weights define a valid distribution (i.e.
sum to 1.0) """
sum = 0
for particle in self.particle_cloud:
sum += particle.w
for particle in self.particle_cloud:
particle.w /= sum
def publish_particles(self, pub):
particles_conv = []
for p in self.particle_cloud:
particles_conv.append(p.as_pose())
# actually send the message so that we can view it in rviz
pub.publish(
PoseArray(header=Header(stamp=rospy.Time.now(), frame_id=self.map_frame), poses=particles_conv))
def scan_received(self, msg):
""" This is the default logic for what to do when processing scan data. Feel free to modify this, however,
I hope it will provide a good guide. The input msg is an object of type sensor_msgs/LaserScan """
if not self.initialized:
# wait for initialization to complete
return
if not (self.tf_listener.canTransform(self.base_frame, msg.header.frame_id, rospy.Time(0))):
# need to know how to transform the laser to the base frame
# this will be given by either Gazebo or neato_node
rospy.logwarn("can't transform to laser scan")
return
if not (self.tf_listener.canTransform(self.base_frame, self.odom_frame, rospy.Time(0))):
# need to know how to transform between base and odometric frames
# this will eventually be published by either Gazebo or neato_node
rospy.logwarn("can't transform to base frame")
return
# calculate pose of laser relative ot the robot base
p = PoseStamped(header = Header(stamp = rospy.Time(0),
frame_id = msg.header.frame_id))
self.laser_pose = self.tf_listener.transformPose(self.base_frame, p)
# find out where the robot thinks it is based on its odometry
p = PoseStamped(header = Header(stamp = rospy.Time(0),
frame_id = self.base_frame))
self.odom_pose = self.tf_listener.transformPose(self.odom_frame, p)
# store the the odometry pose in a more convenient format (x,y,theta)
new_odom_xy_theta = TransformHelpers.convert_pose_to_xy_and_theta(self.odom_pose.pose)
if not self.particle_cloud:
# now that we have all of the necessary transforms we can update the particle cloud
self.initialize_particle_cloud()
# cache the last odometric pose so we can only update our particle filter if we move more than self.d_thresh or self.a_thresh
self.current_odom_xy_theta = new_odom_xy_theta
# update our map to odom transform now that the particles are initialized
self.fix_map_to_odom_transform(msg)
elif (math.fabs(new_odom_xy_theta[0] - self.current_odom_xy_theta[0]) > self.d_thresh or
math.fabs(new_odom_xy_theta[1] - self.current_odom_xy_theta[1]) > self.d_thresh or
math.fabs(new_odom_xy_theta[2] - self.current_odom_xy_theta[2]) > self.a_thresh):
# we have moved far enough to do an update!
self.publish_particles(self.rawcloud_pub)
self.update_particles_with_odom(msg) # update based on odometry
self.publish_particles(self.odomcloud_pub)
self.update_particles_with_laser(msg) # update based on laser scan
self.publish_particles(self.lasercloud_pub)
self.resample_particles() # resample particles to focus on areas of high density
self.update_robot_pose() # update robot's pose
self.fix_map_to_odom_transform(msg) # update map to odom transform now that we have new particles
if self.visualize_weights:
self.visualize_p_weights()
# publish particles (so things like rviz can see them)
self.publish_particles(self.finalcloud_pub)
def fix_map_to_odom_transform(self, msg):
""" Super tricky code to properly update map to odom transform... do not modify this... Difficulty level infinity. """
(translation, rotation) = TransformHelpers.convert_pose_inverse_transform(self.robot_pose)
p = PoseStamped(pose=TransformHelpers.convert_translation_rotation_to_pose(translation, rotation),
header=Header(stamp=rospy.Time(0), frame_id=self.base_frame))
self.odom_to_map = self.tf_listener.transformPose(self.odom_frame, p)
(self.translation, self.rotation) = TransformHelpers.convert_pose_inverse_transform(self.odom_to_map.pose)
def broadcast_last_transform(self):
""" Make sure that we are always broadcasting the last map to odom transformation.
This is necessary so things like move_base can work properly. """
if not (hasattr(self, 'translation') and hasattr(self, 'rotation')):
return
self.tf_broadcaster.sendTransform(self.translation, self.rotation, rospy.get_rostime(), self.odom_frame,
self.map_frame)
def filter_laser(self, ranges):
""" Takes the message from a laser scan as an array and returns a dictionary of angle:distance pairs"""
valid_ranges = {}
for i in range(len(ranges)):
if ranges[i] > 0.0 and ranges[i] < 3.5:
valid_ranges[i] = ranges[i]
return valid_ranges
class Controller():
ActionTakeOff = 0
ActionHover = 1
ActionLand = 2
ActionAnimation = 3
def __init__(self):
self.lastNavdata = None
self.lastState = State.Unknown
rospy.on_shutdown(self.on_shutdown)
rospy.Subscriber("ardrone/navdata", Navdata, self.on_navdata)
self.pubTakeoff = rospy.Publisher('ardrone/takeoff',
Empty,
queue_size=1)
self.pubLand = rospy.Publisher('ardrone/land', Empty, queue_size=1)
self.pubNav = rospy.Publisher('cmd_vel', Twist, queue_size=1)
self.setFlightAnimation = rospy.ServiceProxy(
'ardrone/setflightanimation', FlightAnim)
self.listener = TransformListener()
self.action = Controller.ActionTakeOff
self.pidX = PID(0.2, 0.12, 0.0, -0.3, 0.3, "x")
self.pidY = PID(0.2, 0.12, 0.0, -0.3, 0.3, "y")
self.pidZ = PID(1.0, 0, 0.0, -1.0, 1.0, "z")
self.pidYaw = PID(0.5, 0, 0.0, -0.6, 0.6, "yaw")
# X, Y, Z, Yaw
self.goals = [
[0.0, 0.0, 2.0, math.radians(0)],
"ANIM",
[0.0, 0.0, 2.0, math.radians(0)],
[1.0, 0.0, 1.5, math.radians(90)],
[1.0, 1.0, 0.8, math.radians(180)],
[-1.0, 1.0, 1.2, math.radians(0)],
[1.0, 0.0, 0.8, math.radians(0)],
]
self.goalIndex = 0
def on_navdata(self, data):
self.lastNavdata = data
if data.state != self.lastState:
rospy.loginfo("State Changed: " + str(data.state))
self.lastState = data.state
def on_shutdown(self):
rospy.loginfo("Shutdown: try to land...")
msg = Twist()
for i in range(0, 1000):
self.pubLand.publish()
self.pubNav.publish(msg)
rospy.sleep(1)
def run(self):
while not rospy.is_shutdown():
if self.action == Controller.ActionTakeOff:
if self.lastState == State.Landed:
self.pubTakeoff.publish()
elif self.lastState == State.Hovering or self.lastState == State.Flying or self.lastState == State.Flying2:
self.action = Controller.ActionHover
elif self.action == Controller.ActionLand:
msg = Twist()
self.pubNav.publish(msg)
self.pubLand.publish()
elif self.action == Controller.ActionHover:
rospy.loginfo('pid running')
# transform target world coordinates into local coordinates
targetWorld = PoseStamped()
t = self.listener.getLatestCommonTime(
"/vicon/ar_drone/ar_drone", "/world")
if self.listener.canTransform("/vicon/ar_drone/ar_drone",
"/world", t):
targetWorld.header.stamp = t
targetWorld.header.frame_id = "world"
targetWorld.pose.position.x = self.goals[self.goalIndex][0]
targetWorld.pose.position.y = self.goals[self.goalIndex][1]
targetWorld.pose.position.z = self.goals[self.goalIndex][2]
quaternion = tf.transformations.quaternion_from_euler(
0, 0, self.goals[self.goalIndex][3])
targetWorld.pose.orientation.x = quaternion[0]
targetWorld.pose.orientation.y = quaternion[1]
targetWorld.pose.orientation.z = quaternion[2]
targetWorld.pose.orientation.w = quaternion[3]
targetDrone = self.listener.transformPose(
"/vicon/ar_drone/ar_drone", targetWorld)
quaternion = (targetDrone.pose.orientation.x,
targetDrone.pose.orientation.y,
targetDrone.pose.orientation.z,
targetDrone.pose.orientation.w)
euler = tf.transformations.euler_from_quaternion(
quaternion)
# Run PID controller and send navigation message
msg = Twist()
#msg.linear.x = self.pidX.update(velX, targetVelX)
msg.linear.x = self.pidX.update(
0.0, targetDrone.pose.position.x)
msg.linear.y = self.pidY.update(
0.0, targetDrone.pose.position.y)
msg.linear.z = self.pidZ.update(
0.0, targetDrone.pose.position.z)
msg.angular.z = self.pidYaw.update(0.0, euler[2])
# disable hover mode
msg.angular.x = 1
self.pubNav.publish(msg)
if (math.fabs(targetDrone.pose.position.x) < 0.2
and math.fabs(targetDrone.pose.position.y) < 0.2
and math.fabs(targetDrone.pose.position.z) < 0.2
and math.fabs(euler[2]) < math.radians(20)):
if self.goalIndex < len(self.goals) - 1:
self.goalIndex += 1
if type(self.goals[self.goalIndex]) is str:
msg = Twist()
for i in range(0, 1000):
self.pubNav.publish(msg)
self.setFlightAnimation(8, 0)
rospy.sleep(1.0)
#for i in range(0, 1000):
# self.pubNav.publish(msg)
#rospy.sleep(1.5)
self.goalIndex += 1
rospy.loginfo("Next Goal (X,Y,Z,Yaw): " +
str(self.goals[self.goalIndex]))
else:
pass
#self.action = Controller.ActionLand
rospy.sleep(0.01)
class Controller():
ActionTakeOff = 0
ActionHover = 1
ActionLand = 2
ActionAnimation = 3
def __init__(self):
self.lastNavdata = Navdata()
self.lastImu = Imu()
self.lastMag = Vector3Stamped()
self.current_pose = PoseStamped()
self.current_odom = Odometry()
self.lastState = State.Unknown
self.command = Twist()
self.drone_msg = ARDroneData()
self.cmd_freq = 1.0 / 200.0
self.drone_freq = 1.0 / 200.0
self.action = Controller.ActionTakeOff
self.previousDebugTime = rospy.get_time()
self.pose_error = [0, 0, 0, 0]
self.pidX = PID(0.35, 0.15, 0.025, -1, 1, "x")
self.pidY = PID(0.35, 0.15, 0.025, -1, 1, "y")
self.pidZ = PID(1.5, 0.1, 0.5, -1.0, 1.0, "z")
self.pidYaw = PID(0.75, 0.1, 0.2, -1.0, 1.0, "yaw")
self.scale = 1.0
self.goal = [-1, 0, 0, height, 0] #set it to center to start
self.goal_rate = [0, 0, 0, 0, 0] # Use the update the goal on time
self.current_goal = Goal(
) # Use this to store current goal, reference time-dependent
self.goal_done = False
self.waypoints = None
rospy.on_shutdown(self.on_shutdown)
rospy.Subscriber("ardrone/navdata", Navdata, self.on_navdata)
rospy.Subscriber("ardrone/imu", Imu, self.on_imu)
rospy.Subscriber("ardrone/mag", Vector3Stamped, self.on_mag)
rospy.Subscriber("arcontroller/goal", Goal, self.on_goal)
rospy.Subscriber("arcontroller/waypoints", Waypoints,
self.on_waypoints)
rospy.Subscriber("qualisys/ARDrone/pose", PoseStamped,
self.get_current_pose)
rospy.Subscriber("qualisys/ARDrone/odom", Odometry,
self.get_current_odom)
self.pubTakeoff = rospy.Publisher('ardrone/takeoff',
Empty,
queue_size=1)
self.pubLand = rospy.Publisher('ardrone/land', Empty, queue_size=1)
self.pubCmd = rospy.Publisher('cmd_vel', Twist, queue_size=1)
self.pubDroneData = rospy.Publisher('droneData',
ARDroneData,
queue_size=1)
self.pubGoal = rospy.Publisher('current_goal', Goal, queue_size=1)
self.setFlightAnimation = rospy.ServiceProxy(
'ardrone/setflightanimation', FlightAnim)
self.commandTimer = rospy.Timer(rospy.Duration(self.cmd_freq),
self.sendCommand)
#self.droneDataTimer = rospy.Timer(rospy.Duration(self.drone_freq),self.sendDroneData)
#self.goalTimer = rospy.Timer(rospy.Duration(self.drone_freq),self.sendCurrentGoal)
self.listener = TransformListener()
def get_current_pose(self, data):
self.current_pose = data
def get_current_odom(self, data):
self.current_odom = data
def on_imu(self, data):
self.lastImu = data
def on_mag(self, data):
self.lastMag = data
def on_navdata(self, data):
self.lastNavdata = data
if data.state != self.lastState:
rospy.loginfo("State Changed: " + str(data.state))
self.lastState = data.state
def on_shutdown(self):
rospy.loginfo("Shutdown: try to land...")
self.command = Twist()
for i in range(0, 1000):
self.pubLand.publish()
self.pubCmd.publish(self.command)
rospy.sleep(1)
def on_goal(self, data):
rospy.loginfo('New goal.')
self.goal = [data.t, data.x, data.y, data.z, data.yaw]
self.goal_done = False
def sendCommand(self, event=None):
self.command.linear.x = self.scale * self.pidX.update(
0.0, self.pose_error[0])
self.command.linear.y = self.scale * self.pidY.update(
0.0, self.pose_error[1])
self.command.linear.z = self.pidZ.update(0.0, self.pose_error[2])
self.command.angular.z = self.pidYaw.update(0.0, self.pose_error[3])
self.drone_msg = ARDroneData()
self.drone_msg.header.stamp = rospy.get_rostime()
self.drone_msg.header.frame_id = 'drone_data'
self.drone_msg.cmd = self.command
self.drone_msg.goal.t = rospy.get_time()
self.drone_msg.goal.x = self.goal[1]
self.drone_msg.goal.y = self.goal[2]
self.drone_msg.goal.z = self.goal[3]
self.drone_msg.goal.yaw = self.goal[4]
self.drone_msg.tm = self.lastNavdata.tm
self.pubDroneData.publish(self.drone_msg)
self.pubCmd.publish(self.command)
def sendDroneData(self, event=None):
self.drone_msg = ARDroneData()
self.drone_msg.header.stamp = rospy.get_rostime()
self.drone_msg.header.frame_id = 'drone_data'
#self.drone_msg.navdata = self.lastNavdata
#self.drone_msg.imu = self.lastImu
#self.drone_msg.mag = self.lastMag
#self.drone_msg.pose = self.current_pose
#self.drone_msg.odom = self.current_odom
self.drone_msg.cmd = self.command
self.drone_msg.goal.t = rospy.get_time()
self.drone_msg.goal.x = self.goal[1]
self.drone_msg.goal.y = self.goal[2]
self.drone_msg.goal.z = self.goal[3]
self.drone_msg.goal.yaw = self.goal[4]
self.drone_msg.tm = self.lastNavdata.tm
self.pubDroneData.publish(self.drone_msg)
def sendCurrentGoal(self, event=None):
current_goal = Goal()
current_goal.t = rospy.get_time()
current_goal.x = self.goal[1]
current_goal.y = self.goal[2]
current_goal.z = self.goal[3]
current_goal.yaw = self.goal[4]
self.pubGoal.publish(current_goal)
def on_waypoints(self, data):
rospy.loginfo('New waypoints.')
self.waypoints = []
for d in range(data.len):
self.waypoints.append([
data.waypoints[d].t, data.waypoints[d].x, data.waypoints[d].y,
data.waypoints[d].z, data.waypoints[d].yaw
])
rospy.loginfo(self.waypoints)
def waypoint_follower(self, points):
current_index = 0
next_index = current_index + 1
rospy.loginfo(points)
time_wp = [goal[0] for goal in points]
self.goal = points[current_index] #get the first point
delta_time_wp = time_wp[1] - time_wp[0]
self.current_goal.t = points[0][0]
self.current_goal.x = points[0][1]
self.current_goal.y = points[0][2]
self.current_goal.z = points[0][3]
self.current_goal.yaw = points[0][4]
self.goal_rate = [(points[1][i] - points[0][i]) / delta_time_wp
for i in range(5)]
minX = .05
minY = .05
time0_wp = rospy.get_time()
time_previous_goal = time0_wp
while True: #for i in range(0,points.len()):
#goal = points[i]
# transform target world coordinates into local coordinates
targetWorld = PoseStamped()
t = self.listener.getLatestCommonTime("/ARDrone", "/mocap")
if self.listener.canTransform("/ARDrone", "/mocap", t):
# Get starting time
time_current_goal = rospy.get_time()
diff_time_goal = time_current_goal - time_previous_goal
time_previous_goal = time_current_goal
# Update the continuous goal using rate*t+current_goal
current_goalX = self.goal_rate[1] * diff_time_goal + self.goal[
1]
current_goalY = self.goal_rate[2] * diff_time_goal + self.goal[
2]
current_goalZ = self.goal_rate[3] * diff_time_goal + self.goal[
3]
current_goalYaw = self.goal_rate[
4] * diff_time_goal + self.goal[4]
self.goal = [
time_current_goal - time0_wp, current_goalX, current_goalY,
current_goalZ, current_goalYaw
]
targetWorld.header.stamp = t
targetWorld.header.frame_id = "mocap"
targetWorld.pose.position.x = self.goal[1]
targetWorld.pose.position.y = self.goal[2]
targetWorld.pose.position.z = self.goal[3]
quaternion = tf.transformations.quaternion_from_euler(
0, 0, self.goal[4])
targetWorld.pose.orientation.x = quaternion[0]
targetWorld.pose.orientation.y = quaternion[1]
targetWorld.pose.orientation.z = quaternion[2]
targetWorld.pose.orientation.w = quaternion[3]
targetDrone = self.listener.transformPose(
"/ARDrone", targetWorld)
quaternion = (targetDrone.pose.orientation.x,
targetDrone.pose.orientation.y,
targetDrone.pose.orientation.z,
targetDrone.pose.orientation.w)
euler = tf.transformations.euler_from_quaternion(quaternion)
# Define the pose_error to publish the command in fixed rate
self.pose_error = [
targetDrone.pose.position.x, targetDrone.pose.position.y,
targetDrone.pose.position.z, euler[2]
]
# Run PID controller and send navigation message
# msg = Twist()
# msg.linear.x = self.scale*self.pidX.update(0.0, targetDrone.pose.position.x)
# msg.linear.y = self.scale*self.pidY.update(0.0, targetDrone.pose.position.y)
# msg.linear.z = self.pidZ.update(0.0, targetDrone.pose.position.z)
# msg.angular.z = self.pidYaw.update(0.0, euler[2])
# # disable hover mode
# msg.angular.x = 0
# self.command = msg
#self.pubCmd.publish(msg)
# # WE define the drone data message and publish
# drone_msg = ARDroneData()
# drone_msg.header.stamp = rospy.get_rostime()
# drone_msg.header.frame_id = 'drone_data'
# drone_msg.navdata = self.lastNavdata
# drone_msg.imu = self.lastImu
# drone_msg.mag = self.lastMag
# drone_msg.pose = self.current_pose
# drone_msg.odom = self.current_odom
# drone_msg.cmd = msg
# self.pubDroneData.publish(drone_msg)
error_xy = math.sqrt(targetDrone.pose.position.x**2 +
targetDrone.pose.position.y**2)
current_time_wp = rospy.get_time()
if self.goal[
0] < 0: #-1 implying that waypoints is not time-dependent
# goal t, x, y, z, yaw
#self.goal = points[current_index]
if (error_xy < 0.2
and math.fabs(targetDrone.pose.position.z) < 0.2
and math.fabs(euler[2]) < math.radians(5)):
if (current_index < len(points) - 1):
current_index += 1
self.goal = points[current_index]
else:
return
else:
diff_time_wp = current_time_wp - time0_wp
if diff_time_wp <= time_wp[-1]:
# Check the index of current goal based on rospy time and time vector in waypoints
current_index = next(x for x, val in enumerate(time_wp)
if val >= diff_time_wp)
if current_index >= next_index:
# Meaning current goal is passed, update new goal
if (current_index < len(points) - 1):
next_index = current_index + 1
self.current_goal.t = diff_time_wp
self.current_goal.x = points[current_index][1]
self.current_goal.y = points[current_index][2]
self.current_goal.z = points[current_index][3]
self.current_goal.yaw = points[current_index][
4]
self.goal_rate = [
(points[next_index][i] -
points[current_index][i]) / delta_time_wp
for i in range(5)
]
else:
self.goal_rate = [1, 0, 0, 0, 0]
#self.goal = points[current_index]
#self.current
else:
self.goal = points[-1]
return
#time = rospy.get_time()
diff_time_log = current_time_wp - self.previousDebugTime
if diff_time_log > 0.5:
#log_msg = "Current pos:" + [targetDrone.pose.position.x, targetDrone.pose.position.y,targetDrone.pose.position.z]
rospy.loginfo('--------------------------------------')
rospy.loginfo('Control:%.2f,%.2f,%.2f,%.2f | bat: %.2f',
self.command.linear.x, self.command.linear.y,
self.command.linear.z,
self.command.angular.z,
self.lastNavdata.batteryPercent)
rospy.loginfo(
'Current position: [%.2f, %.2f, %.2f, %.2f, %.2f]',
diff_time_wp, self.current_pose.pose.position.x,
self.current_pose.pose.position.y,
self.current_pose.pose.position.z, euler[2])
rospy.loginfo('Current goal:%.2f,%.2f,%.2f,%.2f,%.2f',
self.goal[0], self.goal[1], self.goal[2],
self.goal[3], self.goal[4])
rospy.loginfo('Error: %.2f,%.2f,%.2f', error_xy,
math.fabs(targetDrone.pose.position.z),
math.fabs(euler[2]))
rospy.loginfo('--------------------------------------')
self.previousDebugTime = current_time_wp
# if (math.fabs(targetDrone.pose.position.x) < 0.1
# and math.fabs(targetDrone.pose.position.y) < 0.1
# and math.fabs(targetDrone.pose.position.z) < 0.2
# and math.fabs(euler[2]) < math.radians(10)):
# if (index < len(points)-1):
# index += 1
# self.goal = points[index]
# else:
# return
# Check time duration and sleep
# durationCmd = self.cmd_freq-(rospy.get_time() - startCmdTime)
# if durationCmd>0: # Meaning the execution < cmd_freq
# rospy.sleep(durationCmd)
def go_to_goal(self, goal):
#rospy.loginfo('Going to goal')
#rospy.loginfo(goal)
self.goal = goal
# transform target world coordinates into local coordinates
targetWorld = PoseStamped()
t = self.listener.getLatestCommonTime("/ARDrone", "/mocap")
if self.listener.canTransform("/ARDrone", "/mocap", t):
# Get starting time
startCmdTime = rospy.get_time()
targetWorld.header.stamp = t
targetWorld.header.frame_id = "mocap"
targetWorld.pose.position.x = goal[1]
targetWorld.pose.position.y = goal[2]
targetWorld.pose.position.z = goal[3]
quaternion = tf.transformations.quaternion_from_euler(
0, 0, goal[4])
targetWorld.pose.orientation.x = quaternion[0]
targetWorld.pose.orientation.y = quaternion[1]
targetWorld.pose.orientation.z = quaternion[2]
targetWorld.pose.orientation.w = quaternion[3]
targetDrone = self.listener.transformPose("/ARDrone", targetWorld)
quaternion = (targetDrone.pose.orientation.x,
targetDrone.pose.orientation.y,
targetDrone.pose.orientation.z,
targetDrone.pose.orientation.w)
euler = tf.transformations.euler_from_quaternion(quaternion)
# Define pose error to publish the command in fixed rate
self.pose_error = [
targetDrone.pose.position.x, targetDrone.pose.position.y,
targetDrone.pose.position.z, euler[2]
]
error_xy = math.sqrt(targetDrone.pose.position.x**2 +
targetDrone.pose.position.y**2)
time = rospy.get_time()
if time - self.previousDebugTime > 1:
#log_msg = "Current pos:" + [targetDrone.pose.position.x, targetDrone.pose.position.y,targetDrone.pose.position.z]
rospy.loginfo('--------------------------------------')
rospy.loginfo('Control:%.2f,%.2f,%.2f,%.2f,bat:%.2f',
self.command.linear.x, self.command.linear.y,
self.command.linear.z, self.command.angular.z,
self.lastNavdata.batteryPercent)
rospy.loginfo('Current goal:%.2f,%.2f,%.2f,%.2f', goal[1],
goal[2], goal[3], goal[4])
rospy.loginfo('Current pose:%.2f,%.2f,%.2f',
self.current_pose.pose.position.x,
self.current_pose.pose.position.y,
self.current_pose.pose.position.z)
rospy.loginfo('Error: %.2f,%.2f,%.2f', error_xy,
math.fabs(targetDrone.pose.position.z),
math.fabs(euler[2]))
self.previousDebugTime = time
if self.goal_done:
rospy.loginfo("Goal done.")
rospy.loginfo('-------------------------------------')
if (error_xy < 0.2 and math.fabs(targetDrone.pose.position.z) < 0.2
and math.fabs(euler[2]) < math.radians(5)):
self.goal_done = True
else:
self.goal_done = False
def run(self):
while not rospy.is_shutdown():
if self.action == Controller.ActionTakeOff:
if self.lastState == State.Landed:
pass
#rospy.loginfo('Taking off.')
#self.pubTakeoff.publish()
elif self.lastState == State.Hovering or self.lastState == State.Flying or self.lastState == State.Flying2:
self.action = Controller.ActionHover
elif self.action == Controller.ActionLand:
msg = Twist()
self.pubCmd.publish(msg)
self.pubLand.publish()
elif self.action == Controller.ActionHover:
if self.waypoints == None:
#if self.goal_done == False:
self.go_to_goal(self.goal)
else:
self.waypoint_follower(self.waypoints)
self.waypoints = None
rospy.sleep(0.01)
class ParticleFilter:
""" The class that represents a Particle Filter ROS Node
Attributes list:
initialized: a Boolean flag to communicate to other class methods that initializaiton is complete
base_frame: the name of the robot base coordinate frame (should be "base_link" for most robots)
map_frame: the name of the map coordinate frame (should be "map" in most cases)
odom_frame: the name of the odometry coordinate frame (should be "odom" in most cases)
scan_topic: the name of the scan topic to listen to (should be "scan" in most cases)
n_particles: the number of particles in the filter
d_thresh: the amount of linear movement before triggering a filter update
a_thresh: the amount of angular movement before triggering a filter update
laser_max_distance: the maximum distance to an obstacle we should use in a likelihood calculation
pose_listener: a subscriber that listens for new approximate pose estimates (i.e. generated through the rviz GUI)
particle_pub: a publisher for the particle cloud
laser_subscriber: listens for new scan data on topic self.scan_topic
tf_listener: listener for coordinate transforms
tf_broadcaster: broadcaster for coordinate transforms
particle_cloud: a list of particles representing a probability distribution over robot poses
current_odom_xy_theta: the pose of the robot in the odometry frame when the last filter update was performed.
The pose is expressed as a list [x,y,theta] (where theta is the yaw)
map: the map we will be localizing ourselves in. The map should be of type nav_msgs/OccupancyGrid
"""
def __init__(self, test = False):
self.test = test
if self.test:
print "Running particle filter in test mode"
else:
print "Running particle filter"
self.initialized = False # make sure we don't perform updates before everything is setup
rospy.init_node('pf') # tell roscore that we are creating a new node named "pf"
self.base_frame = "base_link" # the frame of the robot base
self.map_frame = "map" # the name of the map coordinate frame
self.odom_frame = "odom" # the name of the odometry coordinate frame
self.scan_topic = "scan" # the topic where we will get laser scans from
self.n_particles = 20 # the number of particles to use
self.d_thresh = 0.1 # the amount of linear movement before performing an update
self.a_thresh = math.pi/6 # the amount of angular movement before performing an update
self.laser_max_distance = 2.0 # maximum penalty to assess in the likelihood field model
self.scan_count = 0
self.robot_pose = Pose(position=Point(x=.38, y=.6096,z=0), orientation=Quaternion(x=0, y=0, z=0, w=1))
# Setup pubs and subs
# pose_listener responds to selection of a new approximate robot location (for instance using rviz)
self.pose_listener = rospy.Subscriber("initialpose", PoseWithCovarianceStamped, self.update_initial_pose)
# publish the current particle cloud. This enables viewing particles in rviz.
self.particle_pub = rospy.Publisher("particlecloud", PoseArray)
self.vel_pub = rospy.Publisher('/cmd_vel', Twist, queue_size=10)
# laser_subscriber listens for data from the lidar
self.laser_subscriber = rospy.Subscriber(self.scan_topic, LaserScan, self.scan_received)
# enable listening for and broadcasting coordinate transforms
self.tf_listener = TransformListener()
self.tf_broadcaster = TransformBroadcaster()
self.particle_cloud = []
self.ang_spread = 10.0/180.0 * math.pi
self.lin_spread = .1
self.current_odom_xy_theta = []
#Set up constants for Guassian Probability
self.variance = .1
self.gauss_constant = math.sqrt(2*math.pi)
self.expected_value = 0
# request the map from the map server, the map should be of type nav_msgs/OccupancyGrid
# We make a fake approximate map of an empty 2 by 2 square for testing to keep it simple.
# If this worked better, we'd expand to a real OccupancyGrid, but we never got it to work better.
map = OccupancyGrid()
map.header.frame_id = '/odom'
map.info.map_load_time = rospy.Time.now()
map.info.resolution = .1 # The map resolution [m/cell]
map.info.width = 288
map.info.height = 288
map.data = [[0 for _ in range(map.info.height)] for _ in range(map.info.width)]
for row in range(map.info.height):
map.data[0][row] = 1
map.data[map.info.width-1][row] = 1
for col in range(map.info.width):
map.data[col][0] = 1
map.data[col][map.info.height -1] = 1
self.occupancy_field = OccupancyField(map)
if self.test:
print "Initialized"
self.initialized = True
def update_robot_pose(self):
""" Update the estimate of the robot's pose given the updated particles.
There are two logical methods for this:
(1): compute the mean pose (level 2)
(2): compute the most likely pose (i.e. the mode of the distribution) (level 1)
"""
if self.test:
print "updating robot's pose"
# first make sure that the particle weights are normalized
self.normalize_particles()
total_x = 0.0
total_y = 0.0
total_theta = 0.0
#calculates mean position of particle cloud according to particle weight
#particles are normalized so sum of multiples will return mean
for particle in self.particle_cloud:
total_x += particle.x * particle.w
total_y += particle.y * particle.w
total_theta += particle.theta * particle.w
total_theta = math.cos(total_theta/2)
#set the robot pose to new position
self.robot_pose = Pose(position=Point(x= +total_x, y=total_y,z=0), orientation=Quaternion(x=0, y=0, z=0, w=total_theta))
if self.test:
print "updated pose:"
print self.robot_pose
def update_particles_with_odom(self, msg):
""" Implement a simple version of this (Level 1) or a more complex one (Level 2) """
if self.test:
print "Updating particles from odom"
new_odom_xy_theta = TransformHelpers.convert_pose_to_xy_and_theta(self.odom_pose.pose)
# compute the change in x,y,theta since our last update
if self.current_odom_xy_theta:
old_odom_xy_theta = self.current_odom_xy_theta
delta = (new_odom_xy_theta[0] - self.current_odom_xy_theta[0], new_odom_xy_theta[1] - self.current_odom_xy_theta[1], new_odom_xy_theta[2] - self.current_odom_xy_theta[2])
self.current_odom_xy_theta = new_odom_xy_theta
else:
self.current_odom_xy_theta = new_odom_xy_theta
return
#function moves the particle cloud according to the odometry data
#particle noise is added using gaussian distribution
#standard deviation of gaussian dist was experimentally measured
x_sd = .001
y_sd = .001
theta_sd = .1
for particle in self.particle_cloud:
particle.x += np.random.normal(particle.x + delta[0], x_sd)
particle.y += np.random.normal(particle.y + delta[1], y_sd)
particle.theta += np.random.normal(particle.theta + delta[2], theta_sd)
def map_calc_range(self,x,y,theta):
""" Difficulty Level 3: implement a ray tracing likelihood model... Let me know if you are interested """
# TODO: nothing unless you want to try this alternate likelihood model
pass
def resample_particles(self):
""" Resample the particles according to the new particle weights """
# make sure the distribution is normalized
if self.test:
print "Resampling Particles"
#draw a random sample of particles from particle cloud
#then normalize the remaining particles
weights = [particle.w for particle in self.particle_cloud]
self.particle_cloud = self.draw_random_sample(
self.particle_cloud, weights, self.n_particles)
self.normalize_particles()
def update_particles_with_laser(self, msg):
""" Updates the particle weights in response to the scan contained in the msg """
if self.test:
print "Updating particles from laser scan"
for particle in self.particle_cloud:
#for each particle, get closest object distance and theta
closest_particle_object_distance, closest_particle_object_theta = self.occupancy_field.get_closest_obstacle_distance(particle.x, particle.y)
closest_actual_object_distance = 1000
for i in range(1, 360):
if msg.ranges[i] > 0.0 and msg.ranges[i] < closest_actual_object_distance:
closest_actual_object_distance = msg.ranges[i]
closest_actual_object_theta = (i/360.0)*2*math.pi
#update the particle's weight and theta
particle.w = self.gauss_particle_probability(closest_particle_object_distance-closest_actual_object_distance)
particle.theta = closest_actual_object_theta - closest_particle_object_theta
def gauss_particle_probability(self, difference):
""" Takes the difference between the actual closest object and the closest object to the particle guess and, based on the variance, returns the weight """
return (1/(self.variance*self.gauss_constant))*math.exp(-.5*((difference - self.expected_value)/self.variance)**2)
@staticmethod
def angle_normalize(z):
""" convenience function to map an angle to the range [-pi,pi] """
return math.atan2(math.sin(z), math.cos(z))
@staticmethod
def angle_diff(a, b):
""" Calculates the difference between angle a and angle b (both should be in radians)
the difference is always based on the closest rotation from angle a to angle b
examples:
angle_diff(.1,.2) -> -.1
angle_diff(.1, 2*math.pi - .1) -> .2
angle_diff(.1, .2+2*math.pi) -> -.1
"""
a = ParticleFilter.angle_normalize(a)
b = ParticleFilter.angle_normalize(b)
d1 = a-b
d2 = 2*math.pi - math.fabs(d1)
if d1 > 0:
d2 *= -1.0
if math.fabs(d1) < math.fabs(d2):
return d1
else:
return d2
@staticmethod
def weighted_values(values, probabilities, size):
""" Return a random sample of size elements form the set values with the specified probabilities
values: the values to sample from (numpy.ndarray)
probabilities: the probability of selecting each element in values (numpy.ndarray)
size: the number of samples
"""
bins = np.add.accumulate(probabilities)
return values[np.digitize(random_sample(size), bins)]
@staticmethod
def draw_random_sample(choices, probabilities, n):
""" Return a random sample of n elements from the set choices with the specified probabilities
choices: the values to sample from represented as a list
probabilities: the probability of selecting each index represented as a list
n: the number of samples
"""
values = np.array(range(len(choices)))
probs = np.array(probabilities)
bins = np.add.accumulate(probs)
inds = values[np.digitize(random_sample(n), bins)]
samples = [deepcopy(choices[ind]) for ind in inds]
return samples
def update_initial_pose(self, msg):
""" Callback function to handle re-initializing the particle filter based on a pose estimate.
These pose estimates could be generated by another ROS Node or could come from the rviz GUI """
if self.test:
print "Updating Initial Pose"
xy_theta = TransformHelpers.convert_pose_to_xy_and_theta(msg.pose.pose)
self.initialize_particle_cloud(xy_theta)
self.fix_map_to_odom_transform(msg)
def initialize_particle_cloud(self, xy_theta=None):
""" Initialize the particle cloud.
Arguments
xy_theta: a triple consisting of the mean x, y, and theta (yaw) to initialize the
particle cloud around. If this input is ommitted, the odometry will be used """
if self.test:
print "Initializing Cloud"
if xy_theta == None:
xy_theta = TransformHelpers.convert_pose_to_xy_and_theta(self.robot_pose)
print self.robot_pose
print xy_theta
# raw_input()
self.particle_cloud = []
# TODO create particles
#create a normal distribution of particles around starting position
#then normalize and update pose accordingly
x_vals = np.random.normal(xy_theta[0], self.lin_spread, self.n_particles)
y_vals = np.random.normal(xy_theta[1], self.lin_spread, self.n_particles)
t_vals = np.random.normal(xy_theta[2], self.ang_spread, self.n_particles)
self.particle_cloud = [Particle(x_vals[i], y_vals[i], t_vals[i], 1)
for i in xrange(self.n_particles)]
self.normalize_particles()
self.update_robot_pose()
# TODO(mary): create particles
def normalize_particles(self):
""" Make sure the particle weights define a valid distribution (i.e. sum to 1.0) """
total_weight = sum([particle.w for particle in self.particle_cloud]) * 1.0
for particle in self.particle_cloud:
particle.w /= total_weight
def publish_particles(self, msg):
particles_conv = []
for p in self.particle_cloud:
particles_conv.append(p.as_pose())
# actually send the message so that we can view it in rviz
self.particle_pub.publish(PoseArray(header=Header(stamp=rospy.Time.now(),frame_id=self.map_frame),poses=particles_conv))
def scan_received(self, msg):
self.scan_count +=1
""" This is the default logic for what to do when processing scan data. Feel free to modify this, however,
I hope it will provide a good guide. The input msg is an object of type sensor_msgs/LaserScan """
if not(self.initialized):
# wait for initialization to complete
return
if not(self.tf_listener.canTransform(self.base_frame,msg.header.frame_id,msg.header.stamp)):
# need to know how to transform the laser to the base frame
# this will be given by either Gazebo or neato_node
return
if not(self.tf_listener.canTransform(self.base_frame,self.odom_frame,msg.header.stamp)):
# need to know how to transform between base and odometric frames
# this will eventually be published by either Gazebo or neato_node
return
# calculate pose of laser relative ot the robot base
p = PoseStamped(header=Header(stamp=rospy.Time(0),frame_id=msg.header.frame_id))
self.laser_pose = self.tf_listener.transformPose(self.base_frame,p)
# find out where the robot thinks it is based on its odometry
p = PoseStamped(header=Header(stamp=msg.header.stamp,frame_id=self.base_frame), pose=Pose())
self.odom_pose = self.tf_listener.transformPose(self.odom_frame, p)
# store the the odometry pose in a more convenient format (x,y,theta)
new_odom_xy_theta = TransformHelpers.convert_pose_to_xy_and_theta(self.odom_pose.pose)
if not(self.particle_cloud):
# now that we have all of the necessary transforms we can update the particle cloud
self.initialize_particle_cloud()
# cache the last odometric pose so we can only update our particle filter if we move more than self.d_thresh or self.a_thresh
self.current_odom_xy_theta = new_odom_xy_theta
# update our map to odom transform now that the particles are initialized
self.fix_map_to_odom_transform(msg)
# if self.test or self.scan_count % 1 is 0:
print "updated pose:"
print self.robot_pose
elif (math.fabs(new_odom_xy_theta[0] - self.current_odom_xy_theta[0]) > self.d_thresh or
math.fabs(new_odom_xy_theta[1] - self.current_odom_xy_theta[1]) > self.d_thresh or
math.fabs(new_odom_xy_theta[2] - self.current_odom_xy_theta[2]) > self.a_thresh):
# we have moved far enough to do an update!
self.update_particles_with_odom(msg) # update based on odometry
self.update_particles_with_laser(msg) # update based on laser scan
self.update_robot_pose() # update robot's pose
self.resample_particles() # resample particles to focus on areas of high density
self.fix_map_to_odom_transform(msg) # update map to odom transform now that we have new particles
# publish particles (so things like rviz can see them)
self.publish_particles(msg)
def fix_map_to_odom_transform(self, msg):
""" Super tricky code to properly update map to odom transform... do not modify this... Difficulty level infinity. """
(translation, rotation) = TransformHelpers.convert_pose_inverse_transform(self.robot_pose)
p = PoseStamped(pose=TransformHelpers.convert_translation_rotation_to_pose(translation,rotation),header=Header(stamp=msg.header.stamp,frame_id=self.base_frame))
self.odom_to_map = self.tf_listener.transformPose(self.odom_frame, p)
(self.translation, self.rotation) = TransformHelpers.convert_pose_inverse_transform(self.odom_to_map.pose)
def broadcast_last_transform(self):
""" Make sure that we are always broadcasting the last map to odom transformation.
This is necessary so things like move_base can work properly. """
if not(hasattr(self,'translation') and hasattr(self,'rotation')):
return
self.tf_broadcaster.sendTransform(self.translation, self.rotation, rospy.get_rostime(), self.odom_frame, self.map_frame)
class Controller():
ActionTakeOff = 0
ActionHover = 1
ActionLand = 2
ActionAnimation = 3
def __init__(self):
self.lastNavdata = None
self.lastState = State.Unknown
rospy.on_shutdown(self.on_shutdown)
rospy.Subscriber("ardrone/navdata", Navdata, self.on_navdata)
rospy.Subscriber("arcontroller/goal", Goal , self.on_goal)
rospy.Subscriber("arcontroller/waypoints", Waypoints, self.on_waypoints)
self.pubTakeoff = rospy.Publisher('ardrone/takeoff', Empty, queue_size=1)
self.pubLand = rospy.Publisher('ardrone/land', Empty, queue_size=1)
self.pubNav = rospy.Publisher('cmd_vel', Twist, queue_size=1)
self.setFlightAnimation = rospy.ServiceProxy('ardrone/setflightanimation', FlightAnim)
self.listener = TransformListener()
self.action = Controller.ActionTakeOff
self.pidX = PID(0.2, 0.12, 0.0, -0.3, 0.3, "x")
self.pidY = PID(0.2, 0.12, 0.0, -0.3, 0.3, "y")
self.pidZ = PID(1.0, 0, 0.0, -1.0, 1.0, "z")
self.pidYaw = PID(0.5, 0, 0.0, -0.6, 0.6, "yaw")
# X, Y, Z, Yaw
#self.goals = [
# [0, 0, height, 0.941 ],
# [1.789, 1.158, height, 0.941 ],
# [-2.035, -1.539, height, 0.933 ]
# ]
#self.points_forward = [self.goals[1],self.goals[0],self.goals[2]]
#[[0, 0, height, 0.941 ],
#[-2.035, -1.539, height, 0.933 ]]
#self.goalIndex = 0
self.goal = [0,0,height,0] #set it to center to start
self.goal_done = False
self.waypoints = None
def on_navdata(self, data):
self.lastNavdata = data
if data.state != self.lastState:
rospy.loginfo("State Changed: " + str(data.state))
self.lastState = data.state
def on_shutdown(self):
rospy.loginfo("Shutdown: try to land...")
msg = Twist()
for i in range(0, 1000):
self.pubLand.publish()
self.pubNav.publish(msg)
rospy.sleep(1)
def on_goal(self,data):
rospy.loginfo('New goal.')
self.goal = [data.x,data.y,data.z,data.yaw]
self.goal_done = False
def on_waypoints(self,data):
rospy.loginfo('New waypoints.')
self.waypoints = []
for d in data.waypoints:
self.waypoints.append(d)
def waypoint_follower(self, points):
index = 0
self.goal = points[index] #get the first point
minX = .05
minY = .05
while True:#for i in range(0,points.len()):
#goal = points[i]
# transform target world coordinates into local coordinates
targetWorld = PoseStamped()
t = self.listener.getLatestCommonTime("/vicon/ar_drone/ar_drone", "/world")
if self.listener.canTransform("/vicon/ar_drone/ar_drone", "/world", t):
targetWorld.header.stamp = t
targetWorld.header.frame_id = "world"
targetWorld.pose.position.x = self.goal[0]
targetWorld.pose.position.y = self.goal[1]
targetWorld.pose.position.z = self.goal[2]
quaternion = tf.transformations.quaternion_from_euler(0, 0, self.goal[3])
targetWorld.pose.orientation.x = quaternion[0]
targetWorld.pose.orientation.y = quaternion[1]
targetWorld.pose.orientation.z = quaternion[2]
targetWorld.pose.orientation.w = quaternion[3]
targetDrone = self.listener.transformPose("/vicon/ar_drone/ar_drone", targetWorld)
quaternion = (
targetDrone.pose.orientation.x,
targetDrone.pose.orientation.y,
targetDrone.pose.orientation.z,
targetDrone.pose.orientation.w)
euler = tf.transformations.euler_from_quaternion(quaternion)
# Run PID controller and send navigation message
msg = Twist()
scale = 0.4
msg.linear.x = scale*self.pidX.update(0.0, targetDrone.pose.position.x)
msg.linear.y = scale*self.pidY.update(0.0, targetDrone.pose.position.y)
if (index != points.len()-1):
if (math.fabs(msg.linear.x) < minX) :
if (msg.linear.x < 0) :
msg.linear.x = -minX
elif (msg.linear.x > 0):
msg.linear.x = minX
if (math.fabs(msg.linear.y) < minY) :
if (msg.linear.y < 0) :
msg.linear.y = -minY
elif (msg.linear.y > 0):
msg.linear.y = minY
msg.linear.z = self.pidZ.update(0.0, targetDrone.pose.position.z)
msg.angular.z = self.pidYaw.update(0.0, euler[2])
# disable hover mode
msg.angular.x = 1
self.pubNav.publish(msg)
if (math.fabs(targetDrone.pose.position.x) < 0.2
and math.fabs(targetDrone.pose.position.y) < 0.2
and math.fabs(targetDrone.pose.position.z) < 0.2
and math.fabs(euler[2]) < math.radians(20)):
if (index < points.len()-1):
index += 1
self.goal = points[index]
else:
return
def go_to_goal(self, goal):
#rospy.loginfo('Going to goal')
#rospy.loginfo(goal)
self.goal = goal
# transform target world coordinates into local coordinates
targetWorld = PoseStamped()
t = self.listener.getLatestCommonTime("/vicon/ar_drone/ar_drone", "/world")
if self.listener.canTransform("/vicon/ar_drone/ar_drone", "/world", t):
targetWorld.header.stamp = t
targetWorld.header.frame_id = "world"
targetWorld.pose.position.x = goal[0]
targetWorld.pose.position.y = goal[1]
targetWorld.pose.position.z = goal[2]
quaternion = tf.transformations.quaternion_from_euler(0, 0, goal[3])
targetWorld.pose.orientation.x = quaternion[0]
targetWorld.pose.orientation.y = quaternion[1]
targetWorld.pose.orientation.z = quaternion[2]
targetWorld.pose.orientation.w = quaternion[3]
targetDrone = self.listener.transformPose("/vicon/ar_drone/ar_drone", targetWorld)
quaternion = (
targetDrone.pose.orientation.x,
targetDrone.pose.orientation.y,
targetDrone.pose.orientation.z,
targetDrone.pose.orientation.w)
euler = tf.transformations.euler_from_quaternion(quaternion)
# Run PID controller and send navigation message
msg = Twist()
scale = 0.4
msg.linear.x = scale*self.pidX.update(0.0, targetDrone.pose.position.x)
msg.linear.y = scale*self.pidY.update(0.0, targetDrone.pose.position.y)
msg.linear.z = self.pidZ.update(0.0, targetDrone.pose.position.z)
msg.angular.z = self.pidYaw.update(0.0, euler[2])
# disable hover mode
msg.angular.x = 1
self.pubNav.publish(msg)
if (math.fabs(targetDrone.pose.position.x) < 0.2
and math.fabs(targetDrone.pose.position.y) < 0.2
and math.fabs(targetDrone.pose.position.z) < 0.2
and math.fabs(euler[2]) < math.radians(20)):
self.goal_done = True
rospy.loginfo("Goal done.")
def run(self):
while not rospy.is_shutdown():
if self.action == Controller.ActionTakeOff:
if self.lastState == State.Landed:
#rospy.loginfo('Taking off.')
self.pubTakeoff.publish()
elif self.lastState == State.Hovering or self.lastState == State.Flying or self.lastState == State.Flying2:
self.action = Controller.ActionHover
elif self.action == Controller.ActionLand:
msg = Twist()
self.pubNav.publish(msg)
self.pubLand.publish()
elif self.action == Controller.ActionHover:
if self.waypoints == None:
#if self.goal_done == False:
self.go_to_goal(self.goal)
else:
self.waypoint_follower(self.waypoints)
self.waypoints = None
rospy.sleep(0.01)
class Controller():
Manual = 0
Automatic = 1
TakeOff = 2
Land = 3
def __init__(self):
self.pubNav = rospy.Publisher('cmd_vel', Twist, queue_size=1)
self.listener = TransformListener()
rospy.Subscriber("joy", Joy, self._joyChanged)
rospy.Subscriber("cmd_vel_telop", Twist, self._cmdVelTelopChanged)
self.cmd_vel_telop = Twist()
#self.pidX = PID(20, 12, 0.0, -30, 30, "x")
#self.pidY = PID(-20, -12, 0.0, -30, 30, "y")
#self.pidZ = PID(15000, 3000.0, 1500.0, 10000, 60000, "z")
#self.pidYaw = PID(50.0, 0.0, 0.0, -200.0, 200.0, "yaw")
self.pidX = PID(20, 12, 0.0, -20, 20, "x")
self.pidY = PID(-20, -12, 0.0, -20, 20, "y")
#50000 800
self.pidZ = PID(15000, 3000.0, 1500.0, 10000, 60000, "z")
self.pidYaw = PID(50.0, 0.0, 0.0, -100.0, 100.0, "yaw")
self.state = Controller.Manual
#Target Values
self.pubtarX = rospy.Publisher('target_x', Float32, queue_size=1)
self.pubtarY = rospy.Publisher('target_y', Float32, queue_size=1)
self.pubtarZ = rospy.Publisher('target_z', Float32, queue_size=1)
self.targetX = 0.0
self.targetY = 0.0
self.targetZ = 0.5
self.des_angle = 0.0
#self.power = 50000.0
#Actual Values
self.pubrealX = rospy.Publisher('real_x', Float32, queue_size=1)
self.pubrealY = rospy.Publisher('real_y', Float32, queue_size=1)
self.pubrealZ = rospy.Publisher('real_z', Float32, queue_size=1)
self.lastJoy = Joy()
#Path view
self.pubPath = rospy.Publisher('cf_Uni_path',
MarkerArray,
queue_size=100)
self.path = MarkerArray()
#self.p = []
#Square trajectory
self.square_start = False
self.square_pos = 0
#self.square =[[0.5,0.5,0.5,0.0],
# [0.5,-0.5,0.5,90.0],
# [-0.5,-0.5,0.5,180.0],
# [-0.5,0.5,0.5,270.0]]
#landing flag
self.land_flag = False
self.power = 0.0
def _cmdVelTelopChanged(self, data):
self.cmd_vel_telop = data
if self.state == Controller.Manual:
self.pubNav.publish(data)
def pidReset(self):
self.pidX.reset()
self.pidZ.reset()
self.pidZ.reset()
self.pidYaw.reset()
def square_go(self):
if self.square_start == False:
self.square_pos = 0
self.targetX = square[self.square_pos][0]
self.targetY = square[self.square_pos][1]
self.targetZ = square[self.square_pos][2]
self.des_angle = square[self.square_pos][3]
self.square_pos = self.square_pos + 1
self.square_start = True
else:
self.targetX = square[self.square_pos][0]
self.targetY = square[self.square_pos][1]
self.targetZ = square[self.square_pos][2]
self.des_angle = square[self.square_pos][3]
self.square_pos = self.square_pos + 1
if self.square_pos == 4:
self.square_pos = 0
def _joyChanged(self, data):
if len(data.buttons) == len(self.lastJoy.buttons):
delta = np.array(data.buttons) - np.array(self.lastJoy.buttons)
print("Buton ok")
#Button 1
if delta[0] == 1 and self.state != Controller.Automatic:
print("Automatic!")
self.land_flag = False
#thrust = self.cmd_vel_telop.linear.z
#print(thrust)
self.pidReset()
self.pidZ.integral = 40.0
#self.targetZ = 1
self.state = Controller.Automatic
#Button 2
if delta[1] == 1 and self.state != Controller.Manual:
print("Manual!")
self.land_flag = False
self.pubNav.publish(self.cmd_vel_telop)
self.state = Controller.Manual
#Button 3
if delta[2] == 1:
self.land_flag = False
self.state = Controller.TakeOff
print("TakeOff!")
#Button 4
if delta[3] == 1:
self.land_flag = True
print("Landing!")
self.square_start = False
self.targetX = 0.0
self.targetY = 0.0
self.targetZ = 0.4
self.des_angle = 0.0
self.state = Controller.Automatic
#Button 5
if delta[4] == 1:
self.square_go()
#self.targetX = square[0][0]
#self.targetY = square[0][1]
#self.targetZ = square[0][2]
#self.des_angle = square[0][3]
#print(self.targetZ)
#self.power += 100.0
#print(self.power)
self.state = Controller.Automatic
#Button 6
if delta[5] == 1:
self.square_start = False
self.targetX = 0.0
self.targetY = 0.0
self.targetZ = 0.5
self.des_angle = 0.0
#print(self.targetZ)
#self.power -= 100.0
#print(self.power)
self.state = Controller.Automatic
self.lastJoy = data
def run(self):
thrust = 0
print("jello")
while not rospy.is_shutdown():
if self.state == Controller.TakeOff:
t = self.listener.getLatestCommonTime("/mocap",
"/Nano_Mark_Gon4")
print(
t,
self.listener.canTransform("/mocap", "/Nano_Mark_Gon4", t))
if self.listener.canTransform("/mocap", "/Nano_Mark_Gon4", t):
position, quaternion = self.listener.lookupTransform(
"/mocap", "/Nano_Mark_Gon4", t)
print(position[0], position[1], position[2])
#if position[2] > 2.0 or thrust > 54000:
if thrust > 55000:
self.pidReset()
self.pidZ.integral = thrust / self.pidZ.ki
#self.targetZ = 0.5
self.state = Controller.Automatic
thrust = 0
else:
thrust += 500
#self.power = thrust
msg = self.cmd_vel_telop
msg.linear.z = thrust
self.pubNav.publish(msg)
if self.state == Controller.Land:
t = self.listener.getLatestCommonTime("/mocap",
"/Nano_Mark_Gon4")
if self.listener.canTransform("/mocap", "/Nano_Mark_Gon4", t):
position, quaternion = self.listener.lookupTransform(
"/mocap", "/Nano_Mark_Gon4", t)
if position[2] > 0.05:
msg_land = self.cmd_vel_telop
self.power -= 100
msg_land.linear.z = self.power
self.pubNav.publish(msg_land)
else:
msg_land = self.cmd_vel_telop
msg_land.linear.z = 0
self.pubNav.publish(msg_land)
if self.state == Controller.Automatic:
# transform target world coordinates into local coordinates
t = self.listener.getLatestCommonTime("/mocap",
"/Nano_Mark_Gon4")
print(t)
if self.listener.canTransform("/mocap", "/Nano_Mark_Gon4", t):
position, quaternion = self.listener.lookupTransform(
"/mocap", "/Nano_Mark_Gon4", t)
#print(position[0],position[1],position[2])
euler = tf.transformations.euler_from_quaternion(
quaternion)
print(euler[2] * (180 / math.pi))
msg = self.cmd_vel_telop
#print(self.power)
#Descompostion of the x and y contributions following the Z-Rotation
x_prim = self.pidX.update(0.0, self.targetX - position[0])
y_prim = self.pidY.update(0.0, self.targetY - position[1])
msg.linear.x = x_prim * math.cos(
euler[2]) - y_prim * math.sin(euler[2])
msg.linear.y = x_prim * math.sin(
euler[2]) + y_prim * math.cos(euler[2])
#---old stuff---
#msg.linear.x = self.pidX.update(0.0, 0.0-position[0])
#msg.linear.y = self.pidY.update(0.0,-1.0-position[1])
#msg.linear.z = self.pidZ.update(position[2],1.0)
#z_prim = self.pidZ.update(position[2],self.targetZ)
#print(z_prim)
#if z_prim < self.power:
# msg.linear.z = self.power
#else:
# msg.linear.z = z_prim
#msg.linear.z = self.power
#print(self.power)
msg.linear.z = self.pidZ.update(
0.0, self.targetZ - position[2]
) #self.pidZ.update(position[2], self.targetZ)
msg.angular.z = self.pidYaw.update(
0.0,
self.des_angle * (math.pi / 180) +
euler[2]) #*(180/math.pi))
#msg.angular.z = self.pidYaw.update(0.0,self.des_angle - euler[2])#*(180/math.pi))
print(msg.linear.x, msg.linear.y, msg.linear.z,
msg.angular.z)
#print(euler[2])
#print(msg.angular.z)
self.pubNav.publish(msg)
#Publish Real and Target position
self.pubtarX.publish(self.targetX)
self.pubtarY.publish(self.targetY)
self.pubtarZ.publish(self.targetZ)
self.pubrealX.publish(position[0])
self.pubrealY.publish(position[1])
self.pubrealZ.publish(position[2])
#change square point
if abs(self.targetX-position[0])<0.08 and \
abs(self.targetY-position[1])<0.08 and \
abs(self.targetZ-position[2])<0.08 and \
self.square_start == True:
self.square_go()
#Landing
if abs(self.targetX-position[0])<0.1 and \
abs(self.targetY-position[1])<0.1 and \
abs(self.targetZ-position[2])<0.1 and \
self.land_flag == True:
self.state = Controller.Land
self.power = msg.linear.z
#Publish Path
#point = Marker()
#line = Marker()
#point.header.frame_id = line.header.frame_id = 'mocap'
#POINTS
#point.action = point.ADD
#point.pose.orientation.w = 1.0
#point.id = 0
#point.type = point.POINTS
#point.scale.x = 0.01
#point.scale.y = 0.01
#point.color.g = 1.0
#point.color.a = 1.0
#LINE
#line.action = line.ADD
#line.pose.orientation.w = 1.0
#line.id = 1
#line.type = line.LINE_STRIP
#line.scale.x = 0.01
#line.color.g = 1.0
#line.color.a = 1.0
#p = Point()
#p.x = position[0]
#p.y = position[1]
#p.z = position[2]
#point.points.append(p)
# line.points.append(p)
#self.path.markers.append(p)
#id = 0
#for m in self.path.markers:
# m.id = id
# id += 1
#self.pubPath.publish(self.path)
#self.pubPath.publish(point)
#self.pubPath.publish(line)
point = Marker()
point.header.frame_id = 'mocap'
point.type = point.SPHERE
#points.header.stamp = rospy.Time.now()
point.ns = 'cf_Uni_path'
point.action = point.ADD
#points.id = 0;
point.scale.x = 0.005
point.scale.y = 0.005
point.scale.z = 0.005
point.color.a = 1.0
point.color.r = 1.0
point.color.g = 1.0
point.color.b = 0.0
point.pose.orientation.w = 1.0
point.pose.position.x = position[0]
point.pose.position.y = position[1]
point.pose.position.z = position[2]
self.path.markers.append(point)
id = 0
for m in self.path.markers:
m.id = id
id += 1
self.pubPath.publish(self.path)
#point = Point()
#point.x = position[0]
#point.y = position[1]
#point.z = position[2]
#points.points.append(point)
#self.p.append(pos2path)
#self.path.header.stamp = rospy.Time.now()
#self.path.header.frame_id = 'mocap'
#self.path.poses = self.p
#self.pubPath.publish(points)
rospy.sleep(0.01)
class Multi_circle_1():
def __init__(self, goals):
rospy.init_node('multi_circle_1', anonymous=True)
self.worldFrame = rospy.get_param("~worldFrame", "/world")
self.frame1 = rospy.get_param("~frame1")
#self.frame2 = rospy.get_param("~frame2")
self.radius = rospy.get_param("~radius")
self.x = rospy.get_param("~x")
self.y = rospy.get_param("~y")
self.z = rospy.get_param("~z")
self.freq = rospy.get_param("~freq")
self.lap = rospy.get_param("~lap")
self.pubGoal = rospy.Publisher('goal', PoseStamped, queue_size=1)
self.listener = TransformListener()
self.goals = goals
self.goalIndex = 0
def run(self):
self.listener.waitForTransform(self.worldFrame, self.frame1,
rospy.Time(), rospy.Duration(5.0))
#rospy.loginfo("start running!")
goal = PoseStamped()
goal.header.seq = 0
goal.header.frame_id = self.worldFrame
while not rospy.is_shutdown():
t1 = self.listener.getLatestCommonTime(self.worldFrame,
self.frame1)
if self.listener.canTransform(self.worldFrame, self.frame1, t1):
position, quaternion = self.listener.lookupTransform(
self.worldFrame, self.frame1, t1)
rpy = tf.transformations.euler_from_quaternion(quaternion)
goal.header.seq += 1
goal.header.stamp = rospy.Time.now()
#self.x = position[0]
#self.y = position[1]
#self.z = position[2]+1
goal.pose.position.x = self.x
goal.pose.position.y = self.y
goal.pose.position.z = self.z
quaternion = tf.transformations.quaternion_from_euler(0, 0, 0)
goal.pose.orientation.x = quaternion[0]
goal.pose.orientation.y = quaternion[1]
goal.pose.orientation.z = quaternion[2]
goal.pose.orientation.w = quaternion[3]
self.pubGoal.publish(goal)
break
while not rospy.is_shutdown():
#rospy.loginfo("start running!")
goal.header.seq += 1
goal.header.stamp = rospy.Time.now()
self.pubGoal.publish(goal)
t1 = self.listener.getLatestCommonTime(self.worldFrame,
self.frame1)
if self.listener.canTransform(self.worldFrame, self.frame1, t1):
position, quaternion = self.listener.lookupTransform(
self.worldFrame, self.frame1, t1)
rpy = tf.transformations.euler_from_quaternion(quaternion)
if math.fabs(position[0] - self.x) < 0.15 \
and math.fabs(position[1] - self.y) < 0.15 \
and math.fabs(position[2] - self.z) < 0.15 \
and math.fabs(rpy[2] - 0) < math.radians(10) :
rospy.sleep(3)
break
t_start = rospy.Time.now().to_sec()
#rospy.loginfo("t_start:%lf",t_start)
t_now = t_start
while not rospy.is_shutdown():
goal.header.seq += 1
goal.header.stamp = rospy.Time.now()
goal.pose.position.x = self.x + self.radius * math.sin(
(t_now - t_start) * 2 * math.pi * self.freq)
goal.pose.position.y = self.y + self.radius - self.radius * math.cos(
(t_now - t_start) * 2 * math.pi * self.freq)
goal.pose.position.z = self.z
quaternion = tf.transformations.quaternion_from_euler(0, 0, 0)
goal.pose.orientation.x = quaternion[0]
goal.pose.orientation.y = quaternion[1]
goal.pose.orientation.z = quaternion[2]
goal.pose.orientation.w = quaternion[3]
self.pubGoal.publish(goal)
t_now = rospy.Time.now().to_sec()
rospy.loginfo("t_now-t_start:%lf", t_now - t_start)
class Controller:
Idle = 0
Automatic = 1
TakingOff = 2
Landing = 3
def __init__(self, frame):
self.frame = frame
self.pubNav = rospy.Publisher('cmd_vel', Twist, queue_size=1)
self.listener = TransformListener()
self.pidX = PID(35, 10, 0.0, -20, 20, "x")
self.pidY = PID(-35, -10, -0.0, -20, 20, "y")
self.pidZ = PID(4000, 3000.0, 2000.0, 10000, 60000, "z")
self.pidYaw = PID(-50.0, 0.0, 0.0, -200.0, 200.0, "yaw")
self.state = Controller.Idle
self.goal = Pose()
rospy.Subscriber("goal", Pose, self._poseChanged)
rospy.Service("takeoff", std_srvs.srv.Empty, self._takeoff)
rospy.Service("land", std_srvs.srv.Empty, self._land)
def getTransform(self, source_frame, target_frame):
now = rospy.Time.now()
success = False
if self.listener.canTransform(source_frame, target_frame, rospy.Time(0)):
t = self.listener.getLatestCommonTime(source_frame, target_frame)
if self.listener.canTransform(source_frame, target_frame, t):
position, quaternion = self.listener.lookupTransform(source_frame, target_frame, t)
success = True
delta = (now - t).to_sec() * 1000 #ms
if delta > 25:
rospy.logwarn("Latency: %f ms.", delta)
# self.listener.clear()
# rospy.sleep(0.02)
if success:
return position, quaternion, t
def pidReset(self):
self.pidX.reset()
self.pidZ.reset()
self.pidZ.reset()
self.pidYaw.reset()
def _poseChanged(self, data):
self.goal = data
def _takeoff(self, req):
rospy.loginfo("Takeoff requested!")
self.state = Controller.TakingOff
return std_srvs.srv.EmptyResponse()
def _land(self, req):
rospy.loginfo("Landing requested!")
self.state = Controller.Landing
return std_srvs.srv.EmptyResponse()
def run(self):
thrust = 0
while not rospy.is_shutdown():
now = rospy.Time.now()
if self.state == Controller.TakingOff:
r = self.getTransform("/world", self.frame)
if r:
position, quaternion, t = r
if position[2] > 0.05 or thrust > 50000:
self.pidReset()
self.pidZ.integral = thrust / self.pidZ.ki
self.targetZ = 0.5
self.state = Controller.Automatic
thrust = 0
else:
thrust += 100
msg = Twist()
msg.linear.z = thrust
self.pubNav.publish(msg)
else:
rospy.logerr("Could not transform from /world to %s.", self.frame)
if self.state == Controller.Landing:
self.goal.position.z = 0.05
r = self.getTransform("/world", self.frame)
if r:
position, quaternion, t = r
if position[2] <= 0.1:
self.state = Controller.Idle
msg = Twist()
self.pubNav.publish(msg)
else:
rospy.logerr("Could not transform from /world to %s.", self.frame)
if self.state == Controller.Automatic or self.state == Controller.Landing:
# transform target world coordinates into local coordinates
r = self.getTransform("/world", self.frame)
if r:
position, quaternion, t = r
targetWorld = PoseStamped()
targetWorld.header.stamp = t
targetWorld.header.frame_id = "world"
targetWorld.pose = self.goal
targetDrone = self.listener.transformPose(self.frame, targetWorld)
quaternion = (
targetDrone.pose.orientation.x,
targetDrone.pose.orientation.y,
targetDrone.pose.orientation.z,
targetDrone.pose.orientation.w)
euler = tf.transformations.euler_from_quaternion(quaternion)
msg = Twist()
msg.linear.x = self.pidX.update(0.0, targetDrone.pose.position.x)
msg.linear.y = self.pidY.update(0.0, targetDrone.pose.position.y)
msg.linear.z = self.pidZ.update(0.0, targetDrone.pose.position.z)
msg.angular.z = self.pidYaw.update(0.0, euler[2])
self.pubNav.publish(msg)
else:
rospy.logerr("Could not transform from /world to %s.", self.frame)
if self.state == Controller.Idle:
msg = Twist()
self.pubNav.publish(msg)
rospy.sleep(0.01)
class ParticleFilter:
""" The class that represents a Particle Filter ROS Node
Attributes list:
initialized: a Boolean flag to communicate to other class methods that initializaiton is complete
base_frame: the name of the robot base coordinate frame (should be "base_link" for most robots)
map_frame: the name of the map coordinate frame (should be "map" in most cases)
odom_frame: the name of the odometry coordinate frame (should be "odom" in most cases)
scan_topic: the name of the scan topic to listen to (should be "scan" in most cases)
n_particles: the number of particles in the filter
d_thresh: the amount of linear movement before triggering a filter update
a_thresh: the amount of angular movement before triggering a filter update
laser_max_distance: the maximum distance to an obstacle we should use in a likelihood calculation
pose_listener: a subscriber that listens for new approximate pose estimates (i.e. generated through the rviz GUI)
particle_pub: a publisher for the particle cloud
laser_subscriber: listens for new scan data on topic self.scan_topic
tf_listener: listener for coordinate transforms
tf_broadcaster: broadcaster for coordinate transforms
particle_cloud: a list of particles representing a probability distribution over robot poses
current_odom_xy_theta: the pose of the robot in the odometry frame when the last filter update was performed.
The pose is expressed as a list [x,y,theta] (where theta is the yaw)
map: the map we will be localizing ourselves in. The map should be of type nav_msgs/OccupancyGrid
"""
xy_theta = []
def __init__(self):
self.initialized = False # make sure we don't perform updates before everything is setup
rospy.init_node('pf') # tell roscore that we are creating a new node named "pf"
self.base_frame = "base_link" # the frame of the robot base
self.map_frame = "map" # the name of the map coordinate frame
self.odom_frame = "odom" # the name of the odometry coordinate frame
self.scan_topic = "scan" # the topic where we will get laser scans from
self.n_particles = 250 # the number of particles to use
self.d_thresh = 0.2 # the amount of linear movement before performing an update
self.a_thresh = math.pi/6 # the amount of angular movement before performing an update
self.laser_max_distance = 2.0 # maximum penalty to assess in the likelihood field model
self.model_noise_rate = 0.15
# TODO: define additional constants if needed
# Setup pubs and subs
# pose_listener responds to selection of a new approximate robot location (for instance using rviz)
self.pose_listener = rospy.Subscriber("initialpose", PoseWithCovarianceStamped, self.update_initial_pose)
print()
# publish the current particle cloud. This enables viewing particles in rviz.
self.particle_pub = rospy.Publisher("particlecloud", PoseArray, queue_size=10)
# laser_subscriber listens for data from the lidar
self.laser_subscriber = rospy.Subscriber(self.scan_topic, LaserScan, self.scan_received)
# enable listening for and broadcasting coordinate transforms
self.tf_listener = TransformListener()
self.tf_broadcaster = TransformBroadcaster()
self.particle_cloud = []
self.current_odom_xy_theta = []
# request the map from the map server, the map should be of type nav_msgs/OccupancyGrid
# TODO: fill in the appropriate service call here. The resultant map should be assigned be passed
# into the init method for OccupancyField
rospy.wait_for_service('static_map')
grid = rospy.ServiceProxy('static_map',GetMap)
my_map = grid().map
# for now we have commented out the occupancy field initialization until you can successfully fetch the map
self.field = OccupancyField(my_map)
self.initialized = True
def create_initial_particle_list(self,xy_theta):
init_particle_list = []
n = self.n_particles
for i in range(self.n_particles):
w = 1.0/n
x = gauss(xy_theta[0],0.5)
y = gauss(xy_theta[1],0.5)
theta = gauss(xy_theta[2],((math.pi)/2))
particle = Particle(x,y,theta,w)
init_particle_list.append(particle)
print("init_particle_list")
return init_particle_list
def update_robot_pose(self):
""" Update the estimate of the robot's pose given the updated particles.
There are two logical methods for this:
(1): compute the mean pose
(2): compute the most likely pose (i.e. the mode of the distribution)
"""
# first make sure that the particle weights are normalized
self.normalize_particles()
# TODO: assign the lastest pose into self.robot_pose as a geometry_msgs.Pose object
# just to get started we will fix the robot's pose to always be at the origin
x = 0
y = 0
theta = 0
angles = []
for particle in self.particle_cloud:
x += particle.x * particle.w
y += particle.y * particle.w
v = [particle.w * math.cos(math.radians(particle.theta)), particle.w * math.sin(math.radians(particle.theta))]
angles.append(v)
theta = sum_vectors(angles)
orientation = tf.transformations.quaternion_from_euler(0,0,theta)
self.robot_pose = Pose(position=Point(x=x,y=y),orientation=Quaternion(x=orientation[0], y=orientation[1], z=orientation[2], w=orientation[3]))
def update_particles_with_odom(self, msg):
""" Update the particles using the newly given odometry pose.
The function computes the value delta which is a tuple (x,y,theta)
that indicates the change in position and angle between the odometry
when the particles were last updated and the current odometry.
msg: this is not really needed to implement this, but is here just in case.
"""
print('update_w_odom')
new_odom_xy_theta = convert_pose_to_xy_and_theta(self.odom_pose.pose)
# compute the change in x,y,theta since our last update
if self.current_odom_xy_theta:
old_odom_xy_theta = self.current_odom_xy_theta
delta = (new_odom_xy_theta[0] - self.current_odom_xy_theta[0],
new_odom_xy_theta[1] - self.current_odom_xy_theta[1],
new_odom_xy_theta[2] - self.current_odom_xy_theta[2])
self.current_odom_xy_theta = new_odom_xy_theta
for particle in self.particle_cloud:
parc = (math.atan2(delta[1], delta[0]) - old_odom_xy_theta[2]) % 360
particle.x += (math.sqrt((delta[0]**2) + (delta[1]**2)))* math.cos(parc)
particle.y += (math.sqrt((delta[0]**2) + (delta[1]**2))) * math.sin(parc)
particle.theta += delta[2]
else:
self.current_odom_xy_theta = new_odom_xy_theta
return
#DONE
# TODO: modify particles using delta
# For added difficulty: Implement sample_motion_odometry (Prob Rob p 136)
def map_calc_range(self,x,y,theta):
""" Difficulty Level 3: implement a ray tracing likelihood model... Let me know if you are interested """
# TODO: nothing unless you want to try this alternate likelihood model
pass
def resample_particles(self):
""" Resample the particles according to the new particle weights.
The weights stored with each particle should define the probability that a particular
particle is selected in the resampling step. You may want to make use of the given helper
function draw_random_sample.
"""
# make sure the distribution is normalized
self.normalize_particles()
values = np.empty(self.n_particles)
probs = np.empty(self.n_particles)
for i in range(len(self.particle_cloud)):
values[i] = i
probs[i] = self.particle_cloud[i].w
new_random_particle = ParticleFilter.weighted_values(values,probs,self.n_particles)
new_particles = []
for i in new_random_particle:
idx = int(i)
s_p = self.particle_cloud[idx]
new_particles.append(Particle(x=s_p.x+gauss(0,.025),y=s_p.y+gauss(0,.05),theta=s_p.theta+gauss(0,.05)))
self.particle_cloud = new_particles
self.normalize_particles()
def update_particles_with_laser(self, msg):
""" Updates the particle weights in response to the scan contained in the msg """
print('update_w_laser')
readings = msg.ranges
for particle in self.particle_cloud:
for read in range(0,len(readings),3):
self.field.get_particle_likelyhood(particle,readings[read],self.model_noise_rate,read)
self.normalize_particles()
self.resample_particles()
@staticmethod
def weighted_values(values, probabilities, size):
""" Return a random sample of size elements from the set values with the specified probabilities
values: the values to sample from (numpy.ndarray)
probabilities: the probability of selecting each element in values (numpy.ndarray)
size: the number of samples
"""
bins = np.add.accumulate(probabilities)
return values[np.digitize(random_sample(size), bins)-1]
@staticmethod
def draw_random_sample(choices, probabilities, n):
""" Return a random sample of n elements from the set choices with the specified probabilities
choices: the values to sample from represented as a list
probabilities: the probability of selecting each element in choices represented as a list
n: the number of samples
"""
print('draw_random_sample')
values = np.array(range(len(choices)))
probs = np.array(probabilities)
bins = np.add.accumulate(probs)
inds = values[np.digitize(random_sample(n), bins)]
samples = []
for i in inds:
samples.append(deepcopy(choices[int(i)]))
return samples
def update_initial_pose(self, msg):
""" Callback function to handle re-initializing the particle filter based on a pose estimate.
These pose estimates could be generated by another ROS Node or could come from the rviz GUI """
xy_theta = convert_pose_to_xy_and_theta(msg.pose.pose)
self.initialize_particle_cloud(xy_theta)
self.fix_map_to_odom_transform(msg)
def initialize_particle_cloud(self, xy_theta=None):
""" Initialize the particle cloud.
Arguments
xy_theta: a triple consisting of the mean x, y, and theta (yaw) to initialize the
particle cloud around. If this input is ommitted, the odometry will be used """
if xy_theta == None:
xy_theta = convert_pose_to_xy_and_theta(self.odom_pose.pose)
#self.particle_cloud = []
# TODO create particles
self.particle_cloud = self.create_initial_particle_list(xy_theta)
self.normalize_particles()
self.update_robot_pose()
def normalize_particles(self):
""" Make sure the particle weights define a valid distribution (i.e. sum to 1.0) """
w_sum = sum([p.w for p in self.particle_cloud])
for particle in self.particle_cloud:
particle.w /= w_sum
# TODO: implement this
def publish_particles(self, msg):
print('publish_particles')
particles_conv = []
for p in self.particle_cloud:
particles_conv.append(p.as_pose())
# actually send the message so that we can view it in rviz
self.particle_pub.publish(PoseArray(header=Header(stamp=rospy.Time.now(),
frame_id=self.map_frame),
poses=particles_conv))
def scan_received(self, msg):
""" This is the default logic for what to do when processing scan data.
Feel free to modify this, however, I hope it will provide a good
guide. The input msg is an object of type sensor_msgs/LaserScan """
print('scan_received')
if not(self.initialized):
# wait for initialization to complete
return
if not(self.tf_listener.canTransform(self.base_frame,msg.header.frame_id,msg.header.stamp)):
# need to know how to transform the laser to the base frame
# this will be given by either Gazebo or neato_node
return
if not(self.tf_listener.canTransform(self.base_frame,self.odom_frame,msg.header.stamp)):
# need to know how to transform between base and odometric frames
# this will eventually be published by either Gazebo or neato_node
return
# calculate pose of laser relative ot the robot base
p = PoseStamped(header=Header(stamp=rospy.Time(0),
frame_id=msg.header.frame_id))
self.laser_pose = self.tf_listener.transformPose(self.base_frame,p)
# find out where the robot thinks it is based on its odometry
p = PoseStamped(header=Header(stamp=rospy.Time(0),
frame_id=self.base_frame),
pose=Pose())
self.odom_pose = self.tf_listener.transformPose(self.odom_frame, p)
# store the the odometry pose in a more convenient format (x,y,theta)
new_odom_xy_theta = convert_pose_to_xy_and_theta(self.odom_pose.pose)
if not(self.particle_cloud):
# now that we have all of the necessary transforms we can update the particle cloud
self.initialize_particle_cloud()
# cache the last odometric pose so we can only update our particle filter if we move more than self.d_thresh or self.a_thresh
self.current_odom_xy_theta = new_odom_xy_theta
# update our map to odom transform now that the particles are initialized
self.fix_map_to_odom_transform(msg)
elif (math.fabs(new_odom_xy_theta[0] - self.current_odom_xy_theta[0]) > self.d_thresh or
math.fabs(new_odom_xy_theta[1] - self.current_odom_xy_theta[1]) > self.d_thresh or
math.fabs(new_odom_xy_theta[2] - self.current_odom_xy_theta[2]) > self.a_thresh):
# we have moved far enough to do an update!
self.update_particles_with_odom(msg) # update based on odometry
self.update_particles_with_laser(msg) # update based on laser scan
self.update_robot_pose() # update robot's pose
self.resample_particles() # resample particles to focus on areas of high density
self.fix_map_to_odom_transform(msg) # update map to odom transform now that we have new particles
# publish particles (so things like rviz can see them)
self.publish_particles(msg)
def fix_map_to_odom_transform(self, msg):
""" This method constantly updates the offset of the map and
odometry coordinate systems based on the latest results from
the localizer """
(translation, rotation) = convert_pose_inverse_transform(self.robot_pose)
p = PoseStamped(pose=convert_translation_rotation_to_pose(translation,rotation),
header=Header(stamp=rospy.Time(0),frame_id=self.base_frame))
self.odom_to_map = self.tf_listener.transformPose(self.odom_frame, p)
(self.translation, self.rotation) = convert_pose_inverse_transform(self.odom_to_map.pose)
def broadcast_last_transform(self):
""" Make sure that we are always broadcasting the last map
to odom transformation. This is necessary so things like
move_base can work properly. """
if not(hasattr(self,'translation') and hasattr(self,'rotation')):
return
print('broadcast')
self.tf_broadcaster.sendTransform(self.translation,
self.rotation,
rospy.Time.now(),
self.odom_frame,
self.map_frame)
class Controller():
Manual = 0
Automatic = 1
TakeOff = 2
def __init__(self):
self.pubNav = rospy.Publisher('cmd_vel', Twist, queue_size=1)
self.listener = TransformListener()
rospy.Subscriber("joy", Joy, self._joyChanged)
rospy.Subscriber("cmd_vel_telop", Twist, self._cmdVelTelopChanged)
self.cmd_vel_telop = Twist()
#self.pidX = PID(20, 12, 0.0, -30, 30, "x")
#self.pidY = PID(-20, -12, 0.0, -30, 30, "y")
#self.pidZ = PID(15000, 3000.0, 1500.0, 10000, 60000, "z")
#self.pidYaw = PID(50.0, 0.0, 0.0, -200.0, 200.0, "yaw")
self.pidX = PID(20, 12, 0.0, -20, 20, "x")
self.pidY = PID(-20, -12, 0.0, -20, 20, "y")
#50000 800
self.pidZ = PID(15000, 3000.0, 1500.0, 10000, 60000, "z")
self.pidYaw = PID(50.0, 0.0, 0.0, -100.0, 100.0, "yaw")
self.state = Controller.Manual
#Target Values
self.pubtarX = rospy.Publisher('target_x', Float32, queue_size=1)
self.pubtarY = rospy.Publisher('target_y', Float32, queue_size=1)
self.pubtarZ = rospy.Publisher('target_z', Float32, queue_size=1)
self.targetZ = 0.5
self.targetX = 0.0
self.targetY = 0.0
self.des_angle = 0.0
#self.power = 50000.0
#Actual Values
self.pubrealX = rospy.Publisher('real_x', Float32, queue_size=1)
self.pubrealY = rospy.Publisher('real_y', Float32, queue_size=1)
self.pubrealZ = rospy.Publisher('real_z', Float32, queue_size=1)
self.lastJoy = Joy()
#Path
self.pubPath = rospy.Publisher('cf_Gon_path', Path, queue_size=1)
self.path = Path()
self.p = []
def _cmdVelTelopChanged(self, data):
self.cmd_vel_telop = data
if self.state == Controller.Manual:
self.pubNav.publish(data)
def pidReset(self):
self.pidX.reset()
self.pidZ.reset()
self.pidZ.reset()
self.pidYaw.reset()
def _joyChanged(self, data):
if len(data.buttons) == len(self.lastJoy.buttons):
delta = np.array(data.buttons) - np.array(self.lastJoy.buttons)
print ("Buton ok")
#Button 1
if delta[0] == 1 and self.state != Controller.Automatic:
print("Automatic!")
#thrust = self.cmd_vel_telop.linear.z
#print(thrust)
self.pidReset()
self.pidZ.integral = 40.0
#self.targetZ = 1
self.state = Controller.Automatic
#Button 2
if delta[1] == 1 and self.state != Controller.Manual:
print("Manual!")
self.pubNav.publish(self.cmd_vel_telop)
self.state = Controller.Manual
#Button 3
if delta[2] == 1:
self.state = Controller.TakeOff
print("TakeOff!")
#Button 5
if delta[4] == 1:
#self.targetX = -1.0
#self.targetY = -1.0
self.targetZ = 1.0
self.des_angle = -90.0
#if self.des_angle > 179:
# self.des_angle = -179.0
#print(self.targetZ)
#self.power += 100.0
#print(self.power)
self.state = Controller.Automatic
#Button 6
if delta[5] == 1:
#self.targetX = 1.0
#self.targetY = 1.0
self.targetZ = 0.5
self.des_angle = 90.0
#if self.des_angle < -179:
# self.des_angle = 179.0
#print(self.targetZ)
#self.power -= 100.0
#print(self.power)
self.state = Controller.Automatic
self.lastJoy = data
def run(self):
thrust = 0
print("jello")
while not rospy.is_shutdown():
if self.state == Controller.TakeOff:
t = self.listener.getLatestCommonTime("/mocap", "/Nano_Mark_Gon4")
if self.listener.canTransform("/mocap", "/Nano_Mark_Gon4", t):
position, quaternion = self.listener.lookupTransform("/mocap","/Nano_Mark_Gon4", t)
print(position[0],position[1],position[2])
#if position[2] > 0.1 or thrust > 50000:
if thrust > 51000:
self.pidReset()
self.pidZ.integral = thrust / self.pidZ.ki
#self.targetZ = 0.5
self.state = Controller.Automatic
thrust = 0
else:
thrust += 300
self.power = thrust
msg = self.cmd_vel_telop
msg.linear.z = thrust
self.pubNav.publish(msg)
if self.state == Controller.Automatic:
# transform target world coordinates into local coordinates
t = self.listener.getLatestCommonTime("/mocap","/Nano_Mark_Gon4")
print(t)
if self.listener.canTransform("/mocap","/Nano_Mark_Gon4", t):
position, quaternion = self.listener.lookupTransform("/mocap","/Nano_Mark_Gon4",t)
#print(position[0],position[1],position[2])
euler = tf.transformations.euler_from_quaternion(quaternion)
print(euler[2]*(180/math.pi))
msg = self.cmd_vel_telop
#print(self.power)
#Descompostion of the x and y contributions following the Z-Rotation
x_prim = self.pidX.update(0.0, self.targetX-position[0])
y_prim = self.pidY.update(0.0,self.targetY-position[1])
msg.linear.x = x_prim*math.cos(euler[2]) - y_prim*math.sin(euler[2])
msg.linear.y = x_prim*math.sin(euler[2]) + y_prim*math.cos(euler[2])
#---old stuff---
#msg.linear.x = self.pidX.update(0.0, 0.0-position[0])
#msg.linear.y = self.pidY.update(0.0,-1.0-position[1])
#msg.linear.z = self.pidZ.update(position[2],1.0)
#z_prim = self.pidZ.update(position[2],self.targetZ)
#print(z_prim)
#if z_prim < self.power:
# msg.linear.z = self.power
#else:
# msg.linear.z = z_prim
#msg.linear.z = self.power
#print(self.power)
msg.linear.z = self.pidZ.update(0.0,self.targetZ-position[2]) #self.pidZ.update(position[2], self.targetZ)
msg.angular.z = self.pidYaw.update(0.0,self.des_angle*(math.pi/180) + euler[2])#*(180/math.pi))
#msg.angular.z = self.pidYaw.update(0.0,self.des_angle - euler[2])#*(180/math.pi))
print(msg.linear.x,msg.linear.y,msg.linear.z,msg.angular.z)
#print(euler[2])
#print(msg.angular.z)
self.pubNav.publish(msg)
#Publish Real and Target position
self.pubtarX.publish(self.targetX)
self.pubtarY.publish(self.targetY)
self.pubtarZ.publish(self.targetZ)
self.pubrealX.publish(position[0])
self.pubrealY.publish(position[1])
self.pubrealZ.publish(position[2])
print("que pasaaa")
#Publish Path
pos2path = PoseStamped()
pos2path.pose.position.x = position[0]
pos2path.pose.position.y = position[1]
pos2path.pose.position.z = position[2]
pos2path.pose.orientation.x = quaternion[0]
pos2path.pose.orientation.y = quaternion[1]
pos2path.pose.orientation.z = quaternion[2]
pos2path.pose.orientation.w = quaternion[3]
self.p.append(pos2path)
print('holaaaaa')
self.path.header.frame_id = 'mocap'
self.path.poses = self.p
self.pubPath.publish(self.path)
rospy.sleep(0.01)
rate = rospy.Rate(100)
q_target = None
base_pub = rospy.Publisher('/cmd_vel', Twist)
cmd_vel = Twist()
error_x = 0
error_y = 0
error_rotation = 0
while not rospy.is_shutdown():
gripper_pose = PoseStamped()
gripper_pose.header.frame_id = '/r_gripper_tool_frame'
if (t.canTransform('/base_link', '/r_gripper_tool_frame',
gripper_pose.header.stamp)):
q = t.transformPose('/base_link', gripper_pose)
if not q_target:
q_target = q
error_x = q_target.pose.position.x - q.pose.position.x
error_y = q_target.pose.position.y - q.pose.position.y
error_rotation = q_target.pose.orientation.z - q.pose.orientation.z
print(error_rotation)
cmd_vel.linear.x = -10.0 * error_x
cmd_vel.angular.z = -10.0 * error_y + error_rotation * -5.0
cmd_vel.linear.y = error_rotation * 2.0
base_pub.publish(cmd_vel)
rate.sleep()
class Controller():
Manual = 0
Automatic = 1
TakeOff = 2
def __init__(self):
self.pubNav = rospy.Publisher('cmd_vel', Twist, queue_size=1)
self.listener = TransformListener()
rospy.Subscriber("joy", Joy, self._joyChanged)
rospy.Subscriber("cmd_vel_telop", Twist, self._cmdVelTelopChanged)
self.cmd_vel_telop = Twist()
self.pidX = PID(20, 12, 0.0, -30, 30, "x")
self.pidY = PID(-20, -12, 0.0, -30, 30, "y")
self.pidZ = PID(15000, 3000.0, 1500.0, 10000, 60000, "z")
self.pidYaw = PID(-50.0, 0.0, 0.0, -200.0, 200.0, "yaw")
self.state = Controller.Manual
self.targetZ = 0.5
self.lastJoy = Joy()
def _cmdVelTelopChanged(self, data):
self.cmd_vel_telop = data
if self.state == Controller.Manual:
self.pubNav.publish(data)
def pidReset(self):
self.pidX.reset()
self.pidZ.reset()
self.pidZ.reset()
self.pidYaw.reset()
def _joyChanged(self, data):
if len(data.buttons) == len(self.lastJoy.buttons):
delta = np.array(data.buttons) - np.array(self.lastJoy.buttons)
print ("Buton ok")
#Button 1
if delta[0] == 1 and self.state != Controller.Automatic:
print("Automatic!")
thrust = self.cmd_vel_telop.linear.z
print(thrust)
self.pidReset()
self.pidZ.integral = thrust / self.pidZ.ki
self.targetZ = 0.5
self.state = Controller.Automatic
#Button 2
if delta[1] == 1 and self.state != Controller.Manual:
print("Manual!")
self.pubNav.publish(self.cmd_vel_telop)
self.state = Controller.Manual
#Button 3
if delta[2] == 1:
self.state = Controller.TakeOff
print("TakeOff!")
#Button 5
if delta[4] == 1:
self.targetZ += 0.1
print(self.targetZ)
#Button 6
if delta[5] == 1:
self.targetZ -= 0.1
print(self.targetZ)
self.lastJoy = data
def run(self):
thrust = 0
print("jello")
while not rospy.is_shutdown():
if self.state == Controller.TakeOff:
t = self.listener.getLatestCommonTime("/mocap", "/Nano_Mark")
if self.listener.canTransform("/mocap", "/Nano_Mark", t):
position, quaternion = self.listener.lookupTransform("/mocap", "/Nano_Mark", t)
if position[2] > 0.1 or thrust > 50000:
self.pidReset()
self.pidZ.integral = thrust / self.pidZ.ki
self.targetZ = 0.5
self.state = Controller.Automatic
thrust = 0
else:
thrust += 100
msg = self.cmd_vel_telop
msg.linear.z = thrust
self.pubNav.publish(msg)
if self.state == Controller.Automatic:
# transform target world coordinates into local coordinates
t = self.listener.getLatestCommonTime("/Nano_Mark", "/mocap")
print(t);
if self.listener.canTransform("/Nano_Mark", "/mocap", t):
targetWorld = PoseStamped()
targetWorld.header.stamp = t
targetWorld.header.frame_id = "mocap"
targetWorld.pose.position.x = 0
targetWorld.pose.position.y = 0
targetWorld.pose.position.z = self.targetZ
quaternion = tf.transformations.quaternion_from_euler(0, 0, 0)
targetWorld.pose.orientation.x = quaternion[0]
targetWorld.pose.orientation.y = quaternion[1]
targetWorld.pose.orientation.z = quaternion[2]
targetWorld.pose.orientation.w = quaternion[3]
targetDrone = self.listener.transformPose("/Nano_Mark", targetWorld)
quaternion = (
targetDrone.pose.orientation.x,
targetDrone.pose.orientation.y,
targetDrone.pose.orientation.z,
targetDrone.pose.orientation.w)
euler = tf.transformations.euler_from_quaternion(quaternion)
#msg = self.cmd_vel_teleop
msg.linear.x = self.pidX.update(0.0, targetDrone.pose.position.x)
msg.linear.y = self.pidY.update(0.0, targetDrone.pose.position.y)
msg.linear.z = self.pidZ.update(0.0, targetDrone.pose.position.z) #self.pidZ.update(position[2], self.targetZ)
msg.angular.z = self.pidYaw.update(0.0, euler[2])
#print(euler[2])
#print(msg.angular.z)
self.pubNav.publish(msg)
rospy.sleep(0.01)
class ParticleFilter:
""" The class that represents a Particle Filter ROS Node
Attributes list:
initialized: a Boolean flag to communicate to other class methods that initializaiton is complete
base_frame: the name of the robot base coordinate frame (should be "base_link" for most robots)
map_frame: the name of the map coordinate frame (should be "map" in most cases)
odom_frame: the name of the odometry coordinate frame (should be "odom" in most cases)
scan_topic: the name of the scan topic to listen to (should be "scan" in most cases)
n_particles: the number of particles in the filter
d_thresh: the amount of linear movement before triggering a filter update
a_thresh: the amount of angular movement before triggering a filter update
laser_max_distance: the maximum distance to an obstacle we should use in a likelihood calculation
pose_listener: a subscriber that listens for new approximate pose estimates (i.e. generated through the rviz GUI)
particle_pub: a publisher for the particle cloud
laser_subscriber: listens for new scan data on topic self.scan_topic
tf_listener: listener for coordinate transforms
tf_broadcaster: broadcaster for coordinate transforms
particle_cloud: a list of particles representing a probability distribution over robot poses
current_odom_xy_theta: the pose of the robot in the odometry frame when the last filter update was performed.
The pose is expressed as a list [x,y,theta] (where theta is the yaw)
map: the map we will be localizing ourselves in. The map should be of type nav_msgs/OccupancyGrid
"""
def __init__(self):
self.initialized = False # make sure we don't perform updates before everything is setup
rospy.init_node(
'pf') # tell roscore that we are creating a new node named "pf"
self.base_frame = "base_link" # the frame of the robot base
self.map_frame = "map" # the name of the map coordinate frame
self.odom_frame = "odom" # the name of the odometry coordinate frame
self.scan_topic = "scan" # the topic where we will get laser scans from
self.n_particles = 600 # the number of particles to use
self.particle_init_options = ParticleInitOptions.UNIFORM_DISTRIBUTION
self.d_thresh = 0.1 # the amount of linear movement before performing an update
self.a_thresh = math.pi / 12.0 # the amount of angular movement before performing an update
self.num_lidar_points = 180 # int from 1 to 360
# Note: self.laser_max_distance is NOT implemented
# TODO: Experiment with setting a max acceptable distance for lidar scans
self.laser_max_distance = 2.0 # maximum penalty to assess in the likelihood field model
# Setup pubs and subs
# pose_listener responds to selection of a new approximate robot location (for instance using rviz)
rospy.Subscriber("initialpose", PoseWithCovarianceStamped,
self.update_initial_pose)
# laser_subscriber listens for data from the lidar
rospy.Subscriber(self.scan_topic,
LaserScan,
self.scan_received,
queue_size=1)
# publish the current particle cloud. This enables viewing particles in rviz.
self.particle_pub = rospy.Publisher("particlecloud",
PoseArray,
queue_size=10)
# publish our hypotheses points
self.hypothesis_pub = rospy.Publisher("hypotheses",
MarkerArray,
queue_size=10)
# Publish our hypothesis points right before they get udpated through odom
self.before_odom_hypothesis_pub = rospy.Publisher(
"before_odom_hypotheses", MarkerArray, queue_size=10)
# Publish where the hypothesis points will be once they're updated with the odometry
self.future_hypothesis_pub = rospy.Publisher("future_hypotheses",
MarkerArray,
queue_size=10)
# Publish the lidar scan that pf.py sees
self.lidar_pub = rospy.Publisher("lidar_visualization",
MarkerArray,
queue_size=1)
# Publish the lidar scan projected from the first hypothesis
self.projected_lidar_pub = rospy.Publisher(
"projected_lidar_visualization", MarkerArray, queue_size=1)
# enable listening for and broadcasting coordinate transforms
self.tf_listener = TransformListener()
self.tf_broadcaster = TransformBroadcaster()
self.particle_cloud = []
# change use_projected_stable_scan to True to use point clouds instead of laser scans
self.use_projected_stable_scan = False
self.last_projected_stable_scan = None
if self.use_projected_stable_scan:
# subscriber to the odom point cloud
rospy.Subscriber("projected_stable_scan", PointCloud,
self.projected_scan_received)
self.current_odom_xy_theta = []
self.occupancy_field = OccupancyField()
self.transform_helper = TFHelper()
self.initialized = True
def update_robot_pose(self, timestamp):
""" Update the estimate of the robot's pose given the updated particles.
There are two logical methods for this:
(1): compute the mean pose
(2): compute the most likely pose (i.e. the mode of the distribution)
"""
# assign the best particle's pose to self.robot_pose as a geometry_msgs.Pose object
best_particle = self.particle_cloud[0]
for particle in self.particle_cloud[1:]:
if particle.w > best_particle.w:
best_particle = particle
self.robot_pose = best_particle.as_pose()
self.transform_helper.fix_map_to_odom_transform(
self.robot_pose, timestamp)
def projected_scan_received(self, msg):
self.last_projected_stable_scan = msg
def update_particles_with_odom(self, msg):
""" Update the particles using the newly given odometry pose.
The function computes the value delta which is a tuple (x,y,theta)
that indicates the change in position and angle between the odometry
when the particles were last updated and the current odometry.
msg: this is not really needed to implement this, but is here just in case.
"""
new_odom_xy_theta = self.transform_helper.convert_pose_to_xy_and_theta(
self.odom_pose.pose)
# compute the change in x,y,theta since our last update
if self.current_odom_xy_theta:
old_odom_xy_theta = self.current_odom_xy_theta
delta = (new_odom_xy_theta[0] - self.current_odom_xy_theta[0],
new_odom_xy_theta[1] - self.current_odom_xy_theta[1],
new_odom_xy_theta[2] - self.current_odom_xy_theta[2])
self.current_odom_xy_theta = new_odom_xy_theta
else:
self.current_odom_xy_theta = new_odom_xy_theta
return
# Publish a visualization of all our particles before they get updated
timestamp = rospy.Time.now()
particle_color = (1.0, 0.0, 0.0)
particle_markers = [
particle.as_marker(timestamp, count, "before_odom_hypotheses",
particle_color)
for count, particle in enumerate(self.particle_cloud)
]
# Publish the visualization of all the particles in Rviz
self.before_odom_hypothesis_pub.publish(
MarkerArray(markers=particle_markers))
# delta xy_theta is relative to the odom frame, which is a global frame
# Global Frame -> Robot Frame
# Delta also works for relative to robot _> need to rotate it properly
# Robot Frame - Rotate it so that it's projected from the particle in the particle frame
# Need the difference between the particle theta and the robot theta
# That's how much to rotate it by
# diff_theta = self.current_odom_xy_theta[2] -
# Particle Frame -> Global Frame
for index, particle in enumerate(self.particle_cloud):
diff_theta = self.current_odom_xy_theta[2] - (particle.theta -
math.pi)
partRotMtrx = np.array([[np.cos(diff_theta), -np.sin(diff_theta)],
[np.sin(diff_theta),
np.cos(diff_theta)]])
translationMtrx = np.array([[delta[0]], [delta[1]]])
partTranslationOp = partRotMtrx.dot(translationMtrx)
# update particle position to move with delta
self.particle_cloud[index].x -= partTranslationOp[0, 0]
self.particle_cloud[index].y -= partTranslationOp[1, 0]
self.particle_cloud[index].theta += delta[2]
if len(self.particle_cloud) == 1: # For debugging purposes
print("")
print("Robot Theta: ", self.current_odom_xy_theta[2])
print("Particle Theta:", particle.theta)
print("Diff Theta: ", diff_theta)
print("Deltas before transformations:\nDelta x: ", delta[0],
" | Delta y: ", delta[1], " | Delta theta: ", delta[2])
print("Deltas after transformations:\nDelta x: ",
partTranslationOp[0, 0], " | Delta y: ",
partTranslationOp[1, 0])
# Build up a list of all the just moved particles as Rviz Markers
timestamp = rospy.Time.now()
particle_color = (0.0, 0.0, 1.0)
particle_markers = [
particle.as_marker(timestamp, count, "future_hypotheses",
particle_color)
for count, particle in enumerate(self.particle_cloud)
]
# Publish the visualization of all the particles in Rviz
self.future_hypothesis_pub.publish(
MarkerArray(markers=particle_markers))
def map_calc_range(self, x, y, theta):
""" Difficulty Level 3: implement a ray tracing likelihood model... Let me know if you are interested """
# TODO: nothing unless you want to try this alternate likelihood model
pass
def resample_particles(self):
""" Resample the particles according to the new particle weights.
The weights stored with each particle should define the probability that a particular
particle is selected in the resampling step. You may want to make use of the given helper
function draw_random_sample.
"""
# cull particles
# set looping variable values and initalize array to store significant points
def returnFunc(part):
return part.w
self.particle_cloud.sort(key=returnFunc, reverse=True)
numResamplingNodes = 500
resamplingNodes = self.particle_cloud[0:numResamplingNodes]
# Calculate the number of particles to cluster around each resamplingNode
cluster_size = math.ceil(
(self.n_particles - numResamplingNodes) / numResamplingNodes)
# Uniformly cluster the lowest weighted particles around the highest weighted particles (resamplingNodes)
num_cluster = 0
cluster_radius = 0.25
cluster_theta_range = math.pi / 2.0
for resamplingNode in resamplingNodes:
start_cluster_index = numResamplingNodes + num_cluster * cluster_size
end_cluster_index = start_cluster_index + cluster_size
if end_cluster_index > len(self.particle_cloud):
end_cluster_index = len(self.particle_cloud)
for particle_index in range(start_cluster_index,
end_cluster_index):
self.particle_cloud[particle_index].x = uniform(
(resamplingNode.x - cluster_radius),
(resamplingNode.x + cluster_radius))
self.particle_cloud[particle_index].y = uniform(
(resamplingNode.y - cluster_radius),
(resamplingNode.y + cluster_radius))
self.particle_cloud[particle_index].theta = uniform(
(resamplingNode.w - cluster_theta_range),
(resamplingNode.w + cluster_theta_range))
self.particle_cloud[particle_index].w = resamplingNode.w
# self.particle_cloud[particle_index].w = uniform((resamplingNode.w - cluster_theta_range),(resamplingNode.w + cluster_theta_range))
num_cluster += 1
# TODO: Experiment with clustering points dependending on weight of the resamplingNode
# #repopulate field
# #loop through all the significant weighted particles (or nodes in the probability field)
# nodeIndex = 0
# particleIndex = 0
# while nodeIndex < len(resamplingNodes):
# #place points around nodes
# placePointIndex = 0
# #loop through the number of points that need to be placed given the weight of the particle
# while placePointIndex < self.n_particles * resamplingNodes[nodeIndex].w:
# #place point in circular area around node
# radiusRepopCircle = resamplingNodes[nodeIndex].w*10.0
# #create a point in the circular area
# self.particle_cloud[particleIndex] = Particle(uniform((resamplingNodes[nodeIndex].x - radiusRepopCircle),(resamplingNodes[nodeIndex].x + radiusRepopCircle)),uniform((resamplingNodes[nodeIndex].y - radiusRepopCircle),(resamplingNodes[nodeIndex].y + radiusRepopCircle)),resamplingNodes[nodeIndex].theta)
# #update iteration variables
# particleIndex += 1
# placePointIndex += 1
# nodeIndex += 1
def update_particles_with_laser(self, msg):
""" Updates the particle weights in response to the scan contained in the msg """
# Note: This only updates the weights. This does not move the particles themselves
# Only get the specified number of lidar points at regular slices
downsampled_angle_range_list = []
downsampled_angles = np.linspace(0, 360, self.num_lidar_points, False)
downsampled_angles_int = downsampled_angles.astype(int)
for angle, range_ in enumerate(msg.ranges[0:360]):
if angle in downsampled_angles_int:
downsampled_angle_range_list.append((angle, range_))
# Filter out invalid ranges
filtered_angle_range_list = []
for angle, range_ in downsampled_angle_range_list:
if range_ != 0.0:
filtered_angle_range_list.append((angle, range_))
# Transform ranges into numpy array of xs and ys
relative_to_robot = np.zeros((len(filtered_angle_range_list), 2))
for index, (angle, range_) in enumerate(filtered_angle_range_list):
relative_to_robot[index,
0] = range_ * np.cos(angle * np.pi / 180.0) # xs
relative_to_robot[index,
1] = range_ * np.sin(angle * np.pi / 180.0) # ys
# Build up an array of lidar markers for visualization
lidar_markers = []
for index, xy_point in enumerate(relative_to_robot):
lidar_markers.append(
build_lidar_marker(msg.header.stamp, xy_point[0], xy_point[1],
index, "base_link", "lidar_visualization",
(1.0, 0.0, 0.0)))
# Make sure to delete any old markers
num_deletion_markers = 360 - len(lidar_markers)
for _ in range(num_deletion_markers):
marker_id = len(lidar_markers)
lidar_markers.append(
build_deletion_marker(msg.header.stamp, marker_id,
"lidar_visualization"))
# Publish lidar points for visualization
self.lidar_pub.publish(MarkerArray(markers=lidar_markers))
# For every particle (hypothesis) we have
for particle in self.particle_cloud:
# Combine the xy positions of the scan with the xy w of the hypothesis
# Rotation matrix could be helpful here (https://en.wikipedia.org/wiki/Rotation_matrix)
# Build our rotation matrix
R = np.array([[np.cos(particle.theta), -np.sin(particle.theta)],
[np.sin(particle.theta),
np.cos(particle.theta)]])
# Rotate the points according to particle orientation
relative_to_particle = (R.dot(relative_to_robot.T)).T
# relative_to_particle = relative_to_robot.dot(R)
# Translate points to be relative to map origin
relative_to_map = deepcopy(relative_to_particle)
relative_to_map[:,
0:1] = relative_to_map[:,
0:1] + particle.x * np.ones(
(relative_to_map.
shape[0], 1))
relative_to_map[:,
1:2] = relative_to_map[:,
1:2] + particle.y * np.ones(
(relative_to_map.
shape[0], 1))
# Get the distances of each projected point to nearest obstacle
distance_list = []
for xy_projected_point in relative_to_map:
distance = self.occupancy_field.get_closest_obstacle_distance(
xy_projected_point[0], xy_projected_point[1])
if not np.isfinite(distance):
# Note: ac109 map has approximately a 10x10 bounding box
# Hardcode 1m as the default distance in case the projected point is off the map
distance = 1.0
distance_list.append(distance)
# Calculate a weight for for this particle
# Note: The further away a projected point is from an obstacle point,
# the lower its weight should be
weight = 1.0 / sum(distance_list)
particle.w = weight
# Normalize the weights
self.normalize_particles()
# Grab the first particle
particle = self.particle_cloud[0]
# Visualize the projected points around that particle
projected_lidar_markers = []
for index, xy_point in enumerate(relative_to_map):
projected_lidar_markers.append(
build_lidar_marker(msg.header.stamp, xy_point[0], xy_point[1],
index, "map",
"projected_lidar_visualization"))
# Make sure to delete any old markers
num_deletion_markers = 360 - len(projected_lidar_markers)
for _ in range(num_deletion_markers):
marker_id = len(projected_lidar_markers)
projected_lidar_markers.append(
build_deletion_marker(msg.header.stamp, marker_id,
"projected_lidar_visualization"))
# Publish the projection visualization to rviz
self.projected_lidar_pub.publish(
MarkerArray(markers=projected_lidar_markers))
# Build up a list of all the particles as Rviz Markers
timestamp = rospy.Time.now()
particle_markers = [
particle.as_marker(timestamp, count)
for count, particle in enumerate(n.particle_cloud)
]
# Publish the visualization of all the particles in Rviz
self.hypothesis_pub.publish(MarkerArray(markers=particle_markers))
@staticmethod
def draw_random_sample(choices, probabilities, n):
""" Return a random sample of n elements from the set choices with the specified probabilities
choices: the values to sample from represented as a list
probabilities: the probability of selecting each element in choices represented as a list
n: the number of samples
"""
values = np.array(range(len(choices)))
probs = np.array(probabilities)
bins = np.add.accumulate(probs)
inds = values[np.digitize(random_sample(n), bins)]
samples = []
for i in inds:
samples.append(deepcopy(choices[int(i)]))
return samples
def update_initial_pose(self, msg):
""" Callback function to handle re-initializing the particle filter based on a pose estimate.
These pose estimates could be generated by another ROS Node or could come from the rviz GUI """
xy_theta = self.transform_helper.convert_pose_to_xy_and_theta(
msg.pose.pose)
self.initialize_particle_cloud(msg.header.stamp, xy_theta)
def initialize_particle_cloud(self, timestamp, xy_theta=None):
""" Initialize the particle cloud.
Arguments
xy_theta: a triple consisting of the mean x, y, and theta (yaw) to initialize the
particle cloud around. If this input is omitted, the odometry will be used """
# TODO: Check if moving the xy_theta stuff to where the robot initializes around a given set of points is helpful
# if xy_theta is None:
# xy_theta = self.transform_helper.convert_pose_to_xy_and_theta(self.odom_pose.pose)
# Check how the algorithm should initialize its particles
# Distribute particles uniformly with parameters defining the number of particles and bounding box
if self.particle_init_options == ParticleInitOptions.UNIFORM_DISTRIBUTION:
#create an index to track the x cordinate of the particles being created
#calculate the number of particles to place widthwize vs hightwize along the map based on the number of particles and the dimensions of the map
num_particles_x = math.sqrt(self.n_particles)
num_particles_y = num_particles_x
index_x = -3
#iterate over the map to place points in a uniform grid
while index_x < 4:
index_y = -4
while index_y < 3:
#create a particle at the location with a random orientation
new_particle = Particle(index_x, index_y,
uniform(0, 2 * math.pi))
#add the particle to the particle array
self.particle_cloud.append(new_particle)
#increment the index to place the next particle
index_y += 7 / (num_particles_y)
#increment index to place next column of particles
index_x += 7 / num_particles_x
# Distribute particles uniformly, but hard-coded (mainly for quick tests)
elif self.particle_init_options == ParticleInitOptions.UNIFORM_DISTRIBUTION_HARDCODED:
# Make a list of hypotheses that can update based on values
xs = np.linspace(-3, 4, 21)
ys = np.linspace(-4, 3, 21)
for y in ys:
for x in xs:
for i in range(5):
new_particle = Particle(
x, y, np.random.uniform(0, 2 * math.pi))
self.particle_cloud.append(new_particle)
# Create a single arbitrary particle (For debugging)
elif self.particle_init_options == ParticleInitOptions.SINGLE_PARTICLE:
new_particle = Particle(3.1, 0.0, -0.38802401685700466 + math.pi)
self.particle_cloud.append(new_particle)
# TODO: Set up robot pose on particle cloud initialization
# self.update_robot_pose(timestamp)
def normalize_particles(self):
""" Make sure the particle weights define a valid distribution (i.e. sum to 1.0) """
#set variable inital values
index = 0
weightSum = 0
# calulate the total particle weight
while index < len(self.particle_cloud):
weightSum += self.particle_cloud[index].w
index += 1
index = 0
#normalize the weight for each particle by divifdng by the total weight
while index < len(self.particle_cloud):
self.particle_cloud[
index].w = self.particle_cloud[index].w / weightSum
index += 1
pass
def publish_particles(self, msg):
particles_conv = []
for p in self.particle_cloud:
particles_conv.append(p.as_pose())
# actually send the message so that we can view it in rviz
self.particle_pub.publish(
PoseArray(header=Header(stamp=rospy.Time.now(),
frame_id=self.map_frame),
poses=particles_conv))
def scan_received(self, msg):
""" This is the default logic for what to do when processing scan data.
Feel free to modify this, however, we hope it will provide a good
guide. The input msg is an object of type sensor_msgs/LaserScan """
if not (self.initialized):
# wait for initialization to complete
return
if not (self.tf_listener.canTransform(
self.base_frame, msg.header.frame_id, msg.header.stamp)):
# need to know how to transform the laser to the base frame
# this will be given by either Gazebo or neato_node
return
if not (self.tf_listener.canTransform(self.base_frame, self.odom_frame,
msg.header.stamp)):
# need to know how to transform between base and odometric frames
# this will eventually be published by either Gazebo or neato_node
return
# calculate pose of laser relative to the robot base
p = PoseStamped(
header=Header(stamp=rospy.Time(0), frame_id=msg.header.frame_id))
self.laser_pose = self.tf_listener.transformPose(self.base_frame, p)
# find out where the robot thinks it is based on its odometry
p = PoseStamped(header=Header(stamp=msg.header.stamp,
frame_id=self.base_frame),
pose=Pose())
self.odom_pose = self.tf_listener.transformPose(self.odom_frame, p)
# store the the odometry pose in a more convenient format (x,y,theta)
new_odom_xy_theta = self.transform_helper.convert_pose_to_xy_and_theta(
self.odom_pose.pose)
if not self.current_odom_xy_theta:
self.current_odom_xy_theta = new_odom_xy_theta
return
if not (self.particle_cloud):
# now that we have all of the necessary transforms we can update the particle cloud
self.initialize_particle_cloud(msg.header.stamp)
elif (math.fabs(new_odom_xy_theta[0] - self.current_odom_xy_theta[0]) >
self.d_thresh
or math.fabs(new_odom_xy_theta[1] -
self.current_odom_xy_theta[1]) > self.d_thresh
or math.fabs(new_odom_xy_theta[2] -
self.current_odom_xy_theta[2]) > self.a_thresh):
# we have moved far enough to do an update!
self.update_particles_with_odom(msg) # update based on odometry
if self.last_projected_stable_scan:
last_projected_scan_timeshift = deepcopy(
self.last_projected_stable_scan)
last_projected_scan_timeshift.header.stamp = msg.header.stamp
self.scan_in_base_link = self.tf_listener.transformPointCloud(
"base_link", last_projected_scan_timeshift)
self.update_particles_with_laser(msg) # update based on laser scan
self.update_robot_pose(msg.header.stamp) # update robot's pose
self.resample_particles(
) # resample particles to focus on areas of high density
# publish particles (so things like rviz can see them)
self.publish_particles(msg)
class MyAMCL:
def __init__(self):
self.initialized = False
rospy.init_node('my_amcl')
print "MY AMCL initialized"
# todo make this static
self.n_particles = 100
self.alpha1 = 0.2
self.alpha2 = 0.2
self.alpha3 = 0.2
self.alpha4 = 0.2
self.d_thresh = 0.2
self.a_thresh = math.pi / 6
self.z_hit = 0.5
self.z_rand = 0.5
self.sigma_hit = 0.2
self.laser_max_distance = 2.0
self.laser_subscriber = rospy.Subscriber("scan", LaserScan,
self.scan_received)
self.tf_listener = TransformListener()
self.tf_broadcaster = TransformBroadcaster()
self.particle_pub = rospy.Publisher("particlecloud", PoseArray)
self.particle_cloud = []
self.last_transform_valid = False
self.particle_cloud_initialized = False
self.current_odom_xy_theta = []
# request the map
rospy.wait_for_service("static_map")
static_map = rospy.ServiceProxy("static_map", GetMap)
try:
self.map = static_map().map
except:
print "error receiving map"
self.create_occupancy_field()
self.initialized = True
def create_occupancy_field(self):
X = np.zeros((self.map.info.width * self.map.info.height, 2))
total_occupied = 0
curr = 0
for i in range(self.map.info.width):
for j in range(self.map.info.height):
# occupancy grids are stored in row major order, if you go through this right, you might be able to use curr
ind = i + j * self.map.info.width
if self.map.data[ind] > 0:
total_occupied += 1
X[curr, 0] = float(i)
X[curr, 1] = float(j)
curr += 1
O = np.zeros((total_occupied, 2))
curr = 0
for i in range(self.map.info.width):
for j in range(self.map.info.height):
# occupancy grids are stored in row major order, if you go through this right, you might be able to use curr
ind = i + j * self.map.info.width
if self.map.data[ind] > 0:
O[curr, 0] = float(i)
O[curr, 1] = float(j)
curr += 1
t = time.time()
nbrs = NearestNeighbors(n_neighbors=1, algorithm="ball_tree").fit(O)
distances, indices = nbrs.kneighbors(X)
print time.time() - t
closest_occ = {}
curr = 0
for i in range(self.map.info.width):
for j in range(self.map.info.height):
ind = i + j * self.map.info.width
closest_occ[ind] = distances[curr] * self.map.info.resolution
curr += 1
# this is a bit adhoc, could probably integrate into an internal map structure
self.closest_occ = closest_occ
def update_robot_pose(self):
# first make sure that the particle weights are normalized
self.normalize_particles()
use_mean = True
if use_mean:
mean_x = 0.0
mean_y = 0.0
mean_theta = 0.0
theta_list = []
weighted_orientation_vec = np.zeros((2, 1))
for p in self.particle_cloud:
mean_x += p.x * p.w
mean_y += p.y * p.w
weighted_orientation_vec[0] += p.w * math.cos(p.theta)
weighted_orientation_vec[1] += p.w * math.sin(p.theta)
mean_theta = math.atan2(weighted_orientation_vec[1],
weighted_orientation_vec[0])
self.robot_pose = Particle(x=mean_x, y=mean_y,
theta=mean_theta).as_pose()
else:
weights = []
for p in self.particle_cloud:
weights.append(p.w)
best_particle = np.argmax(weights)
self.robot_pose = self.particle_cloud[best_particle].as_pose()
def update_particles_with_odom(self, msg):
new_odom_xy_theta = MyAMCL.convert_pose_to_xy_and_theta(
self.odom_pose.pose)
if self.current_odom_xy_theta:
old_odom_xy_theta = self.current_odom_xy_theta
delta = (new_odom_xy_theta[0] - self.current_odom_xy_theta[0],
new_odom_xy_theta[1] - self.current_odom_xy_theta[1],
new_odom_xy_theta[2] - self.current_odom_xy_theta[2])
self.current_odom_xy_theta = new_odom_xy_theta
else:
self.current_odom_xy_theta = new_odom_xy_theta
return
# Implement sample_motion_odometry (Prob Rob p 136)
# Avoid computing a bearing from two poses that are extremely near each
# other (happens on in-place rotation).
delta_trans = math.sqrt(delta[0] * delta[0] + delta[1] * delta[1])
if delta_trans < 0.01:
delta_rot1 = 0.0
else:
delta_rot1 = MyAMCL.angle_diff(math.atan2(delta[1], delta[0]),
old_odom_xy_theta[2])
delta_rot2 = MyAMCL.angle_diff(delta[2], delta_rot1)
# We want to treat backward and forward motion symmetrically for the
# noise model to be applied below. The standard model seems to assume
# forward motion.
delta_rot1_noise = min(
math.fabs(MyAMCL.angle_diff(delta_rot1, 0.0)),
math.fabs(MyAMCL.angle_diff(delta_rot1, math.pi)))
delta_rot2_noise = min(
math.fabs(MyAMCL.angle_diff(delta_rot2, 0.0)),
math.fabs(MyAMCL.angle_diff(delta_rot2, math.pi)))
for sample in self.particle_cloud:
# Sample pose differences
delta_rot1_hat = MyAMCL.angle_diff(
delta_rot1,
gauss(
0, self.alpha1 * delta_rot1_noise * delta_rot1_noise +
self.alpha2 * delta_trans * delta_trans))
delta_trans_hat = delta_trans - gauss(
0, self.alpha3 * delta_trans * delta_trans +
self.alpha4 * delta_rot1_noise * delta_rot1_noise +
self.alpha4 * delta_rot2_noise * delta_rot2_noise)
delta_rot2_hat = MyAMCL.angle_diff(
delta_rot2,
gauss(
0, self.alpha1 * delta_rot2_noise * delta_rot2_noise +
self.alpha2 * delta_trans * delta_trans))
# Apply sampled update to particle pose
sample.x += delta_trans_hat * math.cos(sample.theta +
delta_rot1_hat)
sample.y += delta_trans_hat * math.sin(sample.theta +
delta_rot1_hat)
sample.theta += delta_rot1_hat + delta_rot2_hat
def get_map_index(self, x, y):
x_coord = int(
(x - self.map.info.origin.position.x) / self.map.info.resolution)
y_coord = int(
(y - self.map.info.origin.position.y) / self.map.info.resolution)
# check if we are in bounds
if x_coord > self.map.info.width or x_coord < 0:
return float('nan')
if y_coord > self.map.info.height or y_coord < 0:
return float('nan')
ind = x_coord + y_coord * self.map.info.width
if ind >= self.map.info.width * self.map.info.height or ind < 0:
return float('nan')
return ind
def map_calc_range(self, x, y, theta):
''' this is for a beam model... this is pretty damn slow...'''
(x_curr, y_curr) = (x, y)
ind = self.get_map_index(x_curr, y_curr)
while not (math.isnan(ind)):
if self.map.data[ind] > 0:
return math.sqrt((x - x_curr)**2 + (y - y_curr)**2)
x_curr += self.map.info.resolution * 0.5 * math.cos(theta)
y_curr += self.map.info.resolution * 0.5 * math.sin(theta)
ind = self.get_map_index(x_curr, y_curr)
if math.isnan(ind):
return float('nan')
else:
return self.map.info.range_max
def resample_particles(self):
self.normalize_particles()
values = np.empty(self.n_particles)
probs = np.empty(self.n_particles)
for i in range(len(self.particle_cloud)):
values[i] = i
probs[i] = self.particle_cloud[i].w
new_particle_indices = MyAMCL.weighted_values(values, probs,
self.n_particles)
new_particles = []
for i in new_particle_indices:
idx = int(i)
s_p = self.particle_cloud[idx]
new_particles.append(
Particle(x=s_p.x + gauss(0, .025),
y=s_p.y + gauss(0, .025),
theta=s_p.theta + gauss(0, .025)))
self.particle_cloud = new_particles
self.normalize_particles()
def update_particles_with_laser(self, msg):
laser_xy_theta = MyAMCL.convert_pose_to_xy_and_theta(
self.laser_pose.pose)
for p in self.particle_cloud:
adjusted_pose = (p.x + laser_xy_theta[0], p.y + laser_xy_theta[1],
p.theta + laser_xy_theta[2])
# Pre-compute a couple of things
z_hit_denom = 2 * self.sigma_hit**2
z_rand_mult = 1.0 / msg.range_max
# This assumes quite a bit about the weights beforehand (TODO: could base this on p.w)
new_prob = 1.0
for i in range(5, len(msg.ranges), 10):
pz = 1.0
obs_range = msg.ranges[i]
obs_bearing = i * msg.angle_increment + msg.angle_min
if math.isnan(obs_range):
continue
if obs_range >= msg.range_max:
continue
# compute the endpoint of the laser
end_x = p.x + obs_range * math.cos(p.theta + obs_bearing)
end_y = p.y + obs_range * math.sin(p.theta + obs_bearing)
ind = self.get_map_index(end_x, end_y)
if math.isnan(ind):
z = self.laser_max_distance
else:
z = self.closest_occ[ind]
pz += self.z_hit * math.exp(-(z * z) / z_hit_denom)
pz += self.z_rand * z_rand_mult
new_prob += pz**3
p.w = new_prob
@staticmethod
def normalize(z):
return math.atan2(math.sin(z), math.cos(z))
@staticmethod
def angle_diff(a, b):
a = MyAMCL.normalize(a)
b = MyAMCL.normalize(b)
d1 = a - b
d2 = 2 * math.pi - math.fabs(d1)
if d1 > 0:
d2 *= -1.0
if math.fabs(d1) < math.fabs(d2):
return d1
else:
return d2
@staticmethod
def weighted_values(values, probabilities, size):
bins = np.add.accumulate(probabilities)
return values[np.digitize(random_sample(size), bins)]
@staticmethod
def convert_pose_to_xy_and_theta(pose):
orientation_tuple = (pose.orientation.x, pose.orientation.y,
pose.orientation.z, pose.orientation.w)
angles = euler_from_quaternion(orientation_tuple)
return (pose.position.x, pose.position.y, angles[2])
def initialize_particle_cloud(self):
self.particle_cloud_initialized = True
(x, y,
theta) = MyAMCL.convert_pose_to_xy_and_theta(self.odom_pose.pose)
for i in range(self.n_particles):
self.particle_cloud.append(
Particle(x=x + gauss(0, .25),
y=y + gauss(0, .25),
theta=theta + gauss(0, .25)))
self.normalize_particles()
def normalize_particles(self):
z = 0.0
for p in self.particle_cloud:
z += p.w
for i in range(len(self.particle_cloud)):
self.particle_cloud[i].w /= z
def publish_particles(self, msg):
particles_conv = []
for p in self.particle_cloud:
particles_conv.append(p.as_pose())
# actually send the message so that we can view it in rviz
self.particle_pub.publish(
PoseArray(header=Header(stamp=rospy.Time.now(), frame_id="map"),
poses=particles_conv))
def scan_received(self, msg):
if not (self.initialized):
return
if not (self.tf_listener.canTransform(
"base_footprint", msg.header.frame_id, msg.header.stamp)):
return
if not (self.tf_listener.canTransform("base_footprint", "odom",
msg.header.stamp)):
return
p = PoseStamped(
header=Header(stamp=rospy.Time(0), frame_id=msg.header.frame_id))
self.laser_pose = self.tf_listener.transformPose("base_footprint", p)
p = PoseStamped(header=Header(stamp=msg.header.stamp,
frame_id="base_footprint"),
pose=Pose())
#p = PoseStamped(header=Header(stamp=msg.header.stamp,frame_id="base_footprint"), pose=Pose(position=Point(x=0.0,y=0.0,z=0.0),orientation=Quaternion(x=0.0,y=0.0,z=0.0,w=0.0)))
self.odom_pose = self.tf_listener.transformPose("odom", p)
new_odom_xy_theta = MyAMCL.convert_pose_to_xy_and_theta(
self.odom_pose.pose)
if not (self.particle_cloud_initialized):
self.initialize_particle_cloud()
self.update_robot_pose()
self.current_odom_xy_theta = new_odom_xy_theta
self.fix_map_to_odom_transform(msg)
else:
delta = (new_odom_xy_theta[0] - self.current_odom_xy_theta[0],
new_odom_xy_theta[1] - self.current_odom_xy_theta[1],
new_odom_xy_theta[2] - self.current_odom_xy_theta[2])
if math.fabs(delta[0]) > self.d_thresh or math.fabs(
delta[1]) > self.d_thresh or math.fabs(
delta[2]) > self.a_thresh:
self.update_particles_with_odom(msg)
self.update_robot_pose()
self.update_particles_with_laser(msg)
self.resample_particles()
self.update_robot_pose()
self.fix_map_to_odom_transform(msg)
else:
self.fix_map_to_odom_transform(msg, False)
self.publish_particles(msg)
def fix_map_to_odom_transform(self, msg, recompute_odom_to_map=True):
if recompute_odom_to_map:
(translation,
rotation) = MyAMCL.convert_pose_inverse_transform(self.robot_pose)
p = PoseStamped(pose=MyAMCL.convert_translation_rotation_to_pose(
translation, rotation),
header=Header(stamp=msg.header.stamp,
frame_id="base_footprint"))
self.odom_to_map = self.tf_listener.transformPose("odom", p)
(translation, rotation) = MyAMCL.convert_pose_inverse_transform(
self.odom_to_map.pose)
self.tf_broadcaster.sendTransform(
translation, rotation, msg.header.stamp + rospy.Duration(1.0),
"odom", "map")
@staticmethod
def convert_translation_rotation_to_pose(translation, rotation):
return Pose(position=Point(x=translation[0],
y=translation[1],
z=translation[2]),
orientation=Quaternion(x=rotation[0],
y=rotation[1],
z=rotation[2],
w=rotation[3]))
@staticmethod
def convert_pose_inverse_transform(pose):
translation = np.zeros((4, 1))
translation[0] = -pose.position.x
translation[1] = -pose.position.y
translation[2] = -pose.position.z
translation[3] = 1.0
rotation = (pose.orientation.x, pose.orientation.y, pose.orientation.z,
pose.orientation.w)
euler_angle = euler_from_quaternion(rotation)
rotation = np.transpose(rotation_matrix(
euler_angle[2], [0, 0, 1])) # the angle is a yaw
transformed_translation = rotation.dot(translation)
translation = (transformed_translation[0], transformed_translation[1],
transformed_translation[2])
rotation = quaternion_from_matrix(rotation)
return (translation, rotation)
class CF():
def __init__(self, i):
self.worldFrame = rospy.get_param("~worldFrame", "/world")
self.frame = 'crazyflie%d' % i
self.zscale = 3
self.state = 0
self.position = []
self.orientation = []
self.pref = []
self.cmd_vel = []
self.Imu = []
self.goal = PoseStamped()
self.goal.header.seq = 0
self.goal.header.stamp = rospy.Time.now()
pub_name = '/crazyflie%d/goal' % i
sub_name = '/crazyflie%d/CF_state' % i
pub_cmd_diff = '/crazyflie%d/cmd_diff' % i
sub_cmd_vel = '/crazyflie%d/cmd_vel' % i
sub_imu_data = '/crazyflie%d/imu' % i
self.pubGoal = rospy.Publisher(pub_name, PoseStamped, queue_size=1)
self.pubCmd_diff = rospy.Publisher(pub_cmd_diff, Twist, queue_size=1)
self.subCmd_vel = rospy.Subscriber(sub_cmd_vel, Twist,
self.cmdCallback)
self.subGoal = rospy.Subscriber(pub_name, PoseStamped,
self.GoalCallback)
self.subImu = rospy.Subscriber(sub_imu_data, Imu, self.ImuCallback)
self.subState = rospy.Subscriber(sub_name, String, self.CfsCallback)
self.listener = TransformListener()
self.listener.waitForTransform(self.worldFrame, self.frame,
rospy.Time(), rospy.Duration(5.0))
self.updatepos()
self.send_cmd_diff()
def CfsCallback(self, sdata):
self.state = int(sdata.data)
def ImuCallback(self, sdata):
accraw = sdata.linear_acceleration
imuraw = np.array([accraw.x, -accraw.y, -accraw.z])
self.updatepos()
imuraw = cal_R(self.orientation[1], self.orientation[0],
self.orientation[2]).dot(imuraw)
self.Imu = np.array([imuraw[0], imuraw[1], 9.88 + imuraw[2]])
def GoalCallback(self, gdata):
self.goal = gdata
def cmdCallback(self, cdata):
self.cmd_vel = cdata
def hover_init(self, pnext, s):
goal = PoseStamped()
goal.header.seq = self.goal.header.seq + 1
goal.header.frame_id = self.worldFrame
goal.header.stamp = rospy.Time.now()
if self.state != 1:
goal.pose.position.x = pnext[0]
goal.pose.position.y = pnext[1]
goal.pose.position.z = pnext[2]
quaternion = tf.transformations.quaternion_from_euler(0, 0, 0)
goal.pose.orientation.x = quaternion[0]
goal.pose.orientation.y = quaternion[1]
goal.pose.orientation.z = quaternion[2]
goal.pose.orientation.w = quaternion[3]
self.pubGoal.publish(goal)
#print "Waiting for crazyflie %d to take off" %i
else:
t = self.listener.getLatestCommonTime(self.worldFrame, self.frame)
if self.listener.canTransform(self.worldFrame, self.frame, t):
position, quaternion = self.listener.lookupTransform(
self.worldFrame, self.frame, t)
rpy = tf.transformations.euler_from_quaternion(quaternion)
dx = pnext[0] - position[0]
dy = pnext[1] - position[1]
dz = pnext[2] - position[2]
dq = 0 - rpy[2]
s = max(s, 0.5)
goal.pose.position.x = position[0] + s * dx
goal.pose.position.y = position[1] + s * dy
goal.pose.position.z = position[2] + s * dz
quaternion = tf.transformations.quaternion_from_euler(
0, 0, rpy[2] + s * dq)
goal.pose.orientation.x = quaternion[0]
goal.pose.orientation.y = quaternion[1]
goal.pose.orientation.z = quaternion[2]
goal.pose.orientation.w = quaternion[3]
self.pubGoal.publish(goal)
error = sqrt(dx**2 + dy**2 + dz**2)
print 'Hovering error is %0.2f' % error
if error < 0.1:
return 1
return 0
def updatepos(self):
t = self.listener.getLatestCommonTime(self.worldFrame, self.frame)
if self.listener.canTransform(self.worldFrame, self.frame, t):
self.position, quaternion = self.listener.lookupTransform(
self.worldFrame, self.frame, t)
rpy = tf.transformations.euler_from_quaternion(quaternion)
self.orientation = rpy
def goto(self, pnext):
goal = PoseStamped()
goal.header.stamp = rospy.Time.now()
goal.header.seq = self.goal.header.seq + 1
goal.header.frame_id = self.worldFrame
goal.pose.position.x = pnext[0]
goal.pose.position.y = pnext[1]
goal.pose.position.z = pnext[2]
quaternion = tf.transformations.quaternion_from_euler(0, 0, 0)
goal.pose.orientation.x = quaternion[0]
goal.pose.orientation.y = quaternion[1]
goal.pose.orientation.z = quaternion[2]
goal.pose.orientation.w = quaternion[3]
self.pubGoal.publish(goal)
def gotoT(self, pnext, s):
goal = PoseStamped()
goal.header.stamp = rospy.Time.now()
goal.header.seq = self.goal.header.seq + 1
goal.header.frame_id = self.worldFrame
t = self.listener.getLatestCommonTime(self.worldFrame, self.frame)
if self.listener.canTransform(self.worldFrame, self.frame, t):
position, quaternion = self.listener.lookupTransform(
self.worldFrame, self.frame, t)
rpy = tf.transformations.euler_from_quaternion(quaternion)
dx = pnext[0] - position[0]
dy = pnext[1] - position[1]
dz = pnext[2] - position[2]
dq = 0 - rpy[2]
goal.pose.position.x = position[0] + s * dx
goal.pose.position.y = position[1] + s * dy
goal.pose.position.z = position[2] + s * dz
quaternion = tf.transformations.quaternion_from_euler(
0, 0, rpy[2] + s * dq)
goal.pose.orientation.x = quaternion[0]
goal.pose.orientation.y = quaternion[1]
goal.pose.orientation.z = quaternion[2]
goal.pose.orientation.w = quaternion[3]
self.pubGoal.publish(goal)
error = sqrt(dx**2 + dy**2 + dz**2)
print 'error is %0.2f' % error
if error < 0.1:
return 1
else:
return 0
def send_cmd_diff(self, roll=0, pitch=0, yawrate=0, thrust=49000):
# note theoretical default thrust is 39201 equal to 35.89g lifting force
# actual 49000 is 35.89
msg = Twist()
msg.linear.x = -pitch #note vx is -pitch, see crazyflie_server.cpp line 165
msg.linear.y = roll #note vy is roll
msg.linear.z = thrust
msg.angular.z = yawrate
self.pubCmd_diff.publish(msg)
class ParticleFilter:
""" The class that represents a Particle Filter ROS Node
Attributes list:
initialized: a Boolean flag to communicate to other class methods that initializaiton is complete
base_frame: the name of the robot base coordinate frame (should be "base_link" for most robots)
map_frame: the name of the map coordinate frame (should be "map" in most cases)
odom_frame: the name of the odometry coordinate frame (should be "odom" in most cases)
scan_topic: the name of the scan topic to listen to (should be "scan" in most cases)
n_particles: the number of particles in the filter
d_thresh: the amount of linear movement before triggering a filter update
a_thresh: the amount of angular movement before triggering a filter update
laser_max_distance: the maximum distance to an obstacle we should use in a likelihood calculation
pose_listener: a subscriber that listens for new approximate pose estimates (i.e. generated through the rviz GUI)
particle_pub: a publisher for the particle cloud
laser_subscriber: listens for new scan data on topic self.scan_topic
tf_listener: listener for coordinate transforms
tf_broadcaster: broadcaster for coordinate transforms
particle_cloud: a list of particles representing a probability distribution over robot poses
current_odom_xy_theta: the pose of the robot in the odometry frame when the last filter update was performed.
The pose is expressed as a list [x,y,theta] (where theta is the yaw)
map: the map we will be localizing ourselves in. The map should be of type nav_msgs/OccupancyGrid
"""
def __init__(self):
self.initialized = False # make sure we don't perform updates before everything is setup
rospy.init_node('pf') # tell roscore that we are creating a new node named "pf"
self.base_frame = "base_link" # the frame of the robot base
self.map_frame = "map" # the name of the map coordinate frame
self.odom_frame = "odom" # the name of the odometry coordinate frame
self.scan_topic = "scan" # the topic where we will get laser scans from
self.n_particles = 300 # the number of particles to use
self.d_thresh = 0.2 # the amount of linear movement before performing an update
self.a_thresh = math.pi/6 # the amount of angular movement before performing an update
self.laser_max_distance = 2.0 # maximum penalty to assess in the likelihood field model
# TODO: define additional constants if needed
#### DELETE BEFORE POSTING
self.alpha1 = 0.2
self.alpha2 = 0.2
self.alpha3 = 0.2
self.alpha4 = 0.2
self.z_hit = 0.5
self.z_rand = 0.5
self.sigma_hit = 0.1
##### DELETE BEFORE POSTING
# Setup pubs and subs
# pose_listener responds to selection of a new approximate robot location (for instance using rviz)
self.pose_listener = rospy.Subscriber("initialpose", PoseWithCovarianceStamped, self.update_initial_pose)
# publish the current particle cloud. This enables viewing particles in rviz.
self.particle_pub = rospy.Publisher("particlecloud", PoseArray)
# laser_subscriber listens for data from the lidar
self.laser_subscriber = rospy.Subscriber(self.scan_topic, LaserScan, self.scan_received)
# enable listening for and broadcasting coordinate transforms
self.tf_listener = TransformListener()
self.tf_broadcaster = TransformBroadcaster()
self.particle_cloud = []
self.current_odom_xy_theta = []
# request the map
# Difficulty level 2
rospy.wait_for_service("static_map")
static_map = rospy.ServiceProxy("static_map", GetMap)
try:
map = static_map().map
except:
print "error receiving map"
self.occupancy_field = OccupancyField(map)
self.initialized = True
def update_robot_pose(self):
""" Update the estimate of the robot's pose given the updated particles.
There are two logical methods for this:
(1): compute the mean pose
(2): compute the most likely pose (i.e. the mode of the distribution)
"""
""" Difficulty level 2 """
# first make sure that the particle weights are normalized
self.normalize_particles()
use_mean = True
if use_mean:
mean_x = 0.0
mean_y = 0.0
mean_theta = 0.0
theta_list = []
weighted_orientation_vec = np.zeros((2,1))
for p in self.particle_cloud:
mean_x += p.x*p.w
mean_y += p.y*p.w
weighted_orientation_vec[0] += p.w*math.cos(p.theta)
weighted_orientation_vec[1] += p.w*math.sin(p.theta)
mean_theta = math.atan2(weighted_orientation_vec[1],weighted_orientation_vec[0])
self.robot_pose = Particle(x=mean_x,y=mean_y,theta=mean_theta).as_pose()
else:
weights = []
for p in self.particle_cloud:
weights.append(p.w)
best_particle = np.argmax(weights)
self.robot_pose = self.particle_cloud[best_particle].as_pose()
def update_particles_with_odom(self, msg):
""" Implement a simple version of this (Level 1) or a more complex one (Level 2) """
new_odom_xy_theta = TransformHelpers.convert_pose_to_xy_and_theta(self.odom_pose.pose)
if self.current_odom_xy_theta:
old_odom_xy_theta = self.current_odom_xy_theta
delta = (new_odom_xy_theta[0] - self.current_odom_xy_theta[0], new_odom_xy_theta[1] - self.current_odom_xy_theta[1], new_odom_xy_theta[2] - self.current_odom_xy_theta[2])
self.current_odom_xy_theta = new_odom_xy_theta
else:
self.current_odom_xy_theta = new_odom_xy_theta
return
# Implement sample_motion_odometry (Prob Rob p 136)
# Avoid computing a bearing from two poses that are extremely near each
# other (happens on in-place rotation).
delta_trans = math.sqrt(delta[0]*delta[0] + delta[1]*delta[1])
if delta_trans < 0.01:
delta_rot1 = 0.0
else:
delta_rot1 = ParticleFilter.angle_diff(math.atan2(delta[1], delta[0]), old_odom_xy_theta[2])
delta_rot2 = ParticleFilter.angle_diff(delta[2], delta_rot1)
# We want to treat backward and forward motion symmetrically for the
# noise model to be applied below. The standard model seems to assume
# forward motion.
delta_rot1_noise = min(math.fabs(ParticleFilter.angle_diff(delta_rot1, 0.0)), math.fabs(ParticleFilter.angle_diff(delta_rot1, math.pi)));
delta_rot2_noise = min(math.fabs(ParticleFilter.angle_diff(delta_rot2, 0.0)), math.fabs(ParticleFilter.angle_diff(delta_rot2, math.pi)));
for sample in self.particle_cloud:
# Sample pose differences
delta_rot1_hat = ParticleFilter.angle_diff(delta_rot1, gauss(0, self.alpha1*delta_rot1_noise*delta_rot1_noise + self.alpha2*delta_trans*delta_trans))
delta_trans_hat = delta_trans - gauss(0, self.alpha3*delta_trans*delta_trans + self.alpha4*delta_rot1_noise*delta_rot1_noise + self.alpha4*delta_rot2_noise*delta_rot2_noise)
delta_rot2_hat = ParticleFilter.angle_diff(delta_rot2, gauss(0, self.alpha1*delta_rot2_noise*delta_rot2_noise + self.alpha2*delta_trans*delta_trans))
# Apply sampled update to particle pose
sample.x += delta_trans_hat * math.cos(sample.theta + delta_rot1_hat)
sample.y += delta_trans_hat * math.sin(sample.theta + delta_rot1_hat)
sample.theta += delta_rot1_hat + delta_rot2_hat
def map_calc_range(self,x,y,theta):
""" Difficulty Level 3: implement a ray tracing likelihood model... Let me know if you are interested """
pass
def resample_particles(self):
self.normalize_particles()
values = np.empty(self.n_particles)
probs = np.empty(self.n_particles)
for i in range(len(self.particle_cloud)):
values[i] = i
probs[i] = self.particle_cloud[i].w
new_particle_indices = ParticleFilter.weighted_values(values,probs,self.n_particles)
new_particles = []
for i in new_particle_indices:
idx = int(i)
s_p = self.particle_cloud[idx]
new_particles.append(Particle(x=s_p.x+gauss(0,.025),y=s_p.y+gauss(0,.025),theta=s_p.theta+gauss(0,.025)))
self.particle_cloud = new_particles
self.normalize_particles()
# Difficulty level 1
def update_particles_with_laser(self, msg):
""" Updates the particle weights in response to the scan contained in the msg """
laser_xy_theta = TransformHelpers.convert_pose_to_xy_and_theta(self.laser_pose.pose)
for p in self.particle_cloud:
adjusted_pose = (p.x+laser_xy_theta[0], p.y+laser_xy_theta[1], p.theta+laser_xy_theta[2])
# Pre-compute a couple of things
z_hit_denom = 2*self.sigma_hit**2
z_rand_mult = 1.0/msg.range_max
# This assumes quite a bit about the weights beforehand (TODO: could base this on p.w)
new_prob = 1.0 # more agressive DEBUG, was 1.0
for i in range(0,len(msg.ranges),6):
pz = 1.0
obs_range = msg.ranges[i]
obs_bearing = i*msg.angle_increment+msg.angle_min
if math.isnan(obs_range):
continue
if obs_range >= msg.range_max:
continue
# compute the endpoint of the laser
end_x = p.x + obs_range*math.cos(p.theta+obs_bearing)
end_y = p.y + obs_range*math.sin(p.theta+obs_bearing)
z = self.occupancy_field.get_closest_obstacle_distance(end_x,end_y)
if math.isnan(z):
z = self.laser_max_distance
else:
z = z[0] # not sure why this is happening
pz += self.z_hit * math.exp(-(z * z) / z_hit_denom) / (math.sqrt(2*math.pi)*self.sigma_hit)
pz += self.z_rand * z_rand_mult
new_prob += pz**3
p.w = new_prob
pass
@staticmethod
def angle_normalize(z):
""" convenience function to map an angle to the range [-pi,pi] """
return math.atan2(math.sin(z), math.cos(z))
@staticmethod
def angle_diff(a, b):
""" Calculates the difference between angle a and angle b (both should be in radians)
the difference is always based on the closest rotation from angle a to angle b
examples:
angle_diff(.1,.2) -> -.1
angle_diff(.1, 2*math.pi - .1) -> .2
angle_diff(.1, .2+2*math.pi) -> -.1
"""
a = ParticleFilter.angle_normalize(a)
b = ParticleFilter.angle_normalize(b)
d1 = a-b
d2 = 2*math.pi - math.fabs(d1)
if d1 > 0:
d2 *= -1.0
if math.fabs(d1) < math.fabs(d2):
return d1
else:
return d2
@staticmethod
def weighted_values(values, probabilities, size):
""" Return a random sample of size elements form the set values with the specified probabilities
values: the values to sample from (numpy.ndarray)
probabilities: the probability of selecting each element in values (numpy.ndarray)
size: the number of samples
"""
bins = np.add.accumulate(probabilities)
return values[np.digitize(random_sample(size), bins)]
def update_initial_pose(self, msg):
""" Callback function to handle re-initializing the particle filter based on a pose estimate.
These pose estimates could be generated by another ROS Node or could come from the rviz GUI """
xy_theta = TransformHelpers.convert_pose_to_xy_and_theta(msg.pose.pose)
self.initialize_particle_cloud(xy_theta)
self.fix_map_to_odom_transform(msg)
def initialize_particle_cloud(self, xy_theta=None):
""" Initialize the particle cloud.
Arguments
xy_theta: a triple consisting of the mean x, y, and theta (yaw) to initialize the
particle cloud around. If this input is ommitted, the odometry will be used """
if xy_theta == None:
xy_theta = TransformHelpers.convert_pose_to_xy_and_theta(self.odom_pose.pose)
self.particle_cloud = []
for i in range(self.n_particles):
self.particle_cloud.append(Particle(x=xy_theta[0]+gauss(0,.25),y=xy_theta[1]+gauss(0,.25),theta=xy_theta[2]+gauss(0,.25)))
self.normalize_particles()
self.update_robot_pose()
""" Level 1 """
def normalize_particles(self):
""" Make sure the particle weights define a valid distribution (i.e. sum to 1.0) """
z = 0.0
for p in self.particle_cloud:
z += p.w
for i in range(len(self.particle_cloud)):
self.particle_cloud[i].w /= z
def publish_particles(self, msg):
particles_conv = []
for p in self.particle_cloud:
particles_conv.append(p.as_pose())
# actually send the message so that we can view it in rviz
self.particle_pub.publish(PoseArray(header=Header(stamp=rospy.Time.now(),frame_id=self.map_frame),poses=particles_conv))
def scan_received(self, msg):
""" This is the default logic for what to do when processing scan data. Feel free to modify this, however,
I hope it will provide a good guide. The input msg is an object of type sensor_msgs/LaserScan """
if not(self.initialized):
# wait for initialization to complete
return
if not(self.tf_listener.canTransform(self.base_frame,msg.header.frame_id,msg.header.stamp)):
# need to know how to transform the laser to the base frame
# this will be given by either Gazebo or neato_node
return
if not(self.tf_listener.canTransform(self.base_frame,self.odom_frame,msg.header.stamp)):
# need to know how to transform between base and odometric frames
# this will eventually be published by either Gazebo or neato_node
return
# calculate pose of laser relative ot the robot base
p = PoseStamped(header=Header(stamp=rospy.Time(0),frame_id=msg.header.frame_id))
self.laser_pose = self.tf_listener.transformPose(self.base_frame,p)
# find out where the robot thinks it is based on its odometry
p = PoseStamped(header=Header(stamp=msg.header.stamp,frame_id=self.base_frame), pose=Pose())
self.odom_pose = self.tf_listener.transformPose(self.odom_frame, p)
# store the the odometry pose in a more convenient format (x,y,theta)
new_odom_xy_theta = TransformHelpers.convert_pose_to_xy_and_theta(self.odom_pose.pose)
if not(self.particle_cloud):
# now that we have all of the necessary transforms we can update the particle cloud
self.initialize_particle_cloud()
# cache the last odometric pose so we can only update our particle filter if we move more than self.d_thresh or self.a_thresh
self.current_odom_xy_theta = new_odom_xy_theta
# update our map to odom transform now that the particles are initialized
self.fix_map_to_odom_transform(msg)
elif (math.fabs(new_odom_xy_theta[0] - self.current_odom_xy_theta[0]) > self.d_thresh or
math.fabs(new_odom_xy_theta[1] - self.current_odom_xy_theta[1]) > self.d_thresh or
math.fabs(new_odom_xy_theta[2] - self.current_odom_xy_theta[2]) > self.a_thresh):
# we have moved far enough to do an update!
self.update_particles_with_odom(msg) # update based on odometry
self.update_particles_with_laser(msg) # update based on laser scan
self.resample_particles() # resample particles to focus on areas of high density
self.update_robot_pose() # update robot's pose
self.fix_map_to_odom_transform(msg) # update map to odom transform now that we have new particles
# publish particles (so things like rviz can see them)
self.publish_particles(msg)
def fix_map_to_odom_transform(self, msg):
""" Super tricky code to properly update map to odom transform... do not modify this... Difficulty level infinity. """
(translation, rotation) = TransformHelpers.convert_pose_inverse_transform(self.robot_pose)
p = PoseStamped(pose=TransformHelpers.convert_translation_rotation_to_pose(translation,rotation),header=Header(stamp=msg.header.stamp,frame_id=self.base_frame))
self.odom_to_map = self.tf_listener.transformPose(self.odom_frame, p)
(self.translation, self.rotation) = TransformHelpers.convert_pose_inverse_transform(self.odom_to_map.pose)
def broadcast_last_transform(self):
""" Make sure that we are always broadcasting the last map to odom transformation.
This is necessary so things like move_base can work properly. """
if not(hasattr(self,'translation') and hasattr(self,'rotation')):
return
self.tf_broadcaster.sendTransform(self.translation, self.rotation, rospy.get_rostime(), self.odom_frame, self.map_frame)
class Follow():
def __init__(self, goals):
rospy.init_node('follow', anonymous=True)
self.worldFrame = rospy.get_param("~worldFrame", "/world")
self.frame = rospy.get_param("~frame")
self.goal = rospy.get_param("~goal")
self.x = rospy.get_param("~x")
self.y = rospy.get_param("~y")
self.z = rospy.get_param("~z")
self.pubGoal = rospy.Publisher('goal', PoseStamped, queue_size=1)
self.listener = TransformListener()
self.goals = goals
self.goalIndex = 0
def run(self):
self.listener.waitForTransform(self.worldFrame, self.frame,
rospy.Time(), rospy.Duration(5.0))
goal = PoseStamped()
goal.header.seq = 0
goal.header.frame_id = self.worldFrame
while not rospy.is_shutdown():
goal.header.seq += 1
goal.header.stamp = rospy.Time.now()
goal.pose.position.x = self.x
goal.pose.position.y = self.y
goal.pose.position.z = self.z
quaternion = tf.transformations.quaternion_from_euler(0, 0, 0)
goal.pose.orientation.x = quaternion[0]
goal.pose.orientation.y = quaternion[1]
goal.pose.orientation.z = quaternion[2]
goal.pose.orientation.w = quaternion[3]
self.pubGoal.publish(goal)
t = self.listener.getLatestCommonTime(self.worldFrame, self.frame)
if self.listener.canTransform(self.worldFrame, self.frame, t):
position, quaternion = self.listener.lookupTransform(
self.worldFrame, self.frame, t)
rpy = tf.transformations.euler_from_quaternion(quaternion)
#rospy.loginfo(rpy)
if math.fabs(position[0] - self.x) < 0.25 \
and math.fabs(position[1] - self.y) < 0.25 \
and math.fabs(position[2] - self.z) < 0.25 \
and math.fabs(rpy[2] - 0) < math.radians(10):
rospy.sleep(3)
self.goalIndex += 1
break
while not rospy.is_shutdown():
goal.header.seq += 1
goal.header.stamp = rospy.Time.now()
quaternion = tf.transformations.quaternion_from_euler(0, 0, 0)
goal.pose.orientation.x = quaternion[0]
goal.pose.orientation.y = quaternion[1]
goal.pose.orientation.z = quaternion[2]
goal.pose.orientation.w = quaternion[3]
t = self.listener.getLatestCommonTime(self.worldFrame, self.goal)
if self.listener.canTransform(self.worldFrame, self.goal, t):
position, quaternion = self.listener.lookupTransform(
self.worldFrame, self.goal, t)
goal.pose.position.x = position[0] - 0.8 * math.sin(rpy[2])
goal.pose.position.y = position[1] + 0.8 * math.cos(rpy[2])
goal.pose.position.z = position[2] + 0.5
rpy = tf.transformations.euler_from_quaternion(quaternion)
#rospy.loginfo(rpy)
self.pubGoal.publish(goal)
class ParticleFilter:
""" The class that represents a Particle Filter ROS Node
Attributes list:
initialized: a Boolean flag to communicate to other class methods that initializaiton is complete
base_frame: the name of the robot base coordinate frame (should be "base_link" for most robots)
map_frame: the name of the map coordinate frame (should be "map" in most cases)
odom_frame: the name of the odometry coordinate frame (should be "odom" in most cases)
scan_topic: the name of the scan topic to listen to (should be "scan" in most cases)
n_particles: the number of particles in the filter
d_thresh: the amount of linear movement before triggering a filter update
a_thresh: the amount of angular movement before triggering a filter update
laser_max_distance: the maximum distance to an obstacle we should use in a likelihood calculation
pose_listener: a subscriber that listens for new approximate pose estimates (i.e. generated through the rviz GUI)
particle_pub: a publisher for the particle cloud
laser_subscriber: listens for new scan data on topic self.scan_topic
tf_listener: listener for coordinate transforms
tf_broadcaster: broadcaster for coordinate transforms
particle_cloud: a list of particles representing a probability distribution over robot poses
current_odom_xy_theta: the pose of the robot in the odometry frame when the last filter update was performed.
The pose is expressed as a list [x,y,theta] (where theta is the yaw)
map: the map we will be localizing ourselves in. The map should be of type nav_msgs/OccupancyGrid
"""
def __init__(self):
self.initialized = False # make sure we don't perform updates before everything is setup
rospy.init_node(
'pf') # tell roscore that we are creating a new node named "pf"
self.base_frame = "base_link" # the frame of the robot base
self.map_frame = "map" # the name of the map coordinate frame
self.odom_frame = "odom" # the name of the odometry coordinate frame
self.scan_topic = "scan" # the topic where we will get laser scans from
self.n_particles = 300 # the number of particles to use
self.d_thresh = 0.2 # the amount of linear movement before performing an update
self.a_thresh = math.pi / 6 # the amount of angular movement before performing an update
self.laser_max_distance = 2.0 # maximum penalty to assess in the likelihood field model
# Setup pubs and subs
# pose_listener responds to selection of a new approximate robot location (for instance using rviz)
rospy.Subscriber("initialpose", PoseWithCovarianceStamped,
self.update_initial_pose)
# publish the current particle cloud. This enables viewing particles in rviz.
self.particle_pub = rospy.Publisher("particlecloud",
PoseArray,
queue_size=10)
self.weight_pub = rospy.Publisher('visualization_marker',
MarkerArray,
queue_size=10)
# laser_subscriber listens for data from the lidar
rospy.Subscriber(self.scan_topic, LaserScan, self.scan_received)
# enable listening for and broadcasting coordinate transforms
self.tf_listener = TransformListener()
self.tf_broadcaster = TransformBroadcaster()
# Holds all particles
self.particle_cloud = []
# Holds pre-normalized probabilities for each particle
self.scan_probabilities = []
# change use_projected_stable_scan to True to use point clouds instead of laser scans
self.use_projected_stable_scan = False
self.last_projected_stable_scan = None
if self.use_projected_stable_scan:
# subscriber to the odom point cloud
rospy.Subscriber("projected_stable_scan", PointCloud,
self.projected_scan_received)
self.current_odom_xy_theta = []
self.occupancy_field = OccupancyField()
self.transform_helper = TFHelper()
self.initialized = True
def update_robot_pose(self, timestamp):
""" Update the estimate of the robot's pose given the updated particles.
There are two logical methods for this:
(1): compute the mean pose
(2): compute the most likely pose (i.e. the mode of the distribution)
"""
# first make sure that the particle weights are normalized
self.normalize_particles()
# Calculate the mean pose
if self.particle_cloud:
mean_x, mean_y, mean_theta = 0, 0, 0
for particle in self.particle_cloud:
mean_x += particle.x
mean_y += particle.y
mean_theta += particle.theta
mean_x /= len(self.particle_cloud)
mean_y /= len(self.particle_cloud)
mean_theta /= len(self.particle_cloud)
self.robot_pose = Particle(mean_x, mean_y, mean_theta).as_pose()
else:
self.robot_pose = Pose()
self.transform_helper.fix_map_to_odom_transform(
self.robot_pose, timestamp)
def projected_scan_received(self, msg):
self.last_projected_stable_scan = msg
def update_particles_with_odom(self, msg):
""" Update the particles using the newly given odometry pose.
The function computes the value delta which is a tuple (x,y,theta)
that indicates the change in position and angle between the odometry
when the particles were last updated and the current odometry.
msg: this is not really needed to implement this, but is here just in case.
"""
new_odom_xy_theta = self.transform_helper.convert_pose_to_xy_and_theta(
self.odom_pose.pose)
# compute the change in x,y,theta since our last update
if self.current_odom_xy_theta:
old_odom_xy_theta = self.current_odom_xy_theta
delta = (new_odom_xy_theta[0] - self.current_odom_xy_theta[0],
new_odom_xy_theta[1] - self.current_odom_xy_theta[1],
new_odom_xy_theta[2] - self.current_odom_xy_theta[2])
self.current_odom_xy_theta = new_odom_xy_theta
else:
self.current_odom_xy_theta = new_odom_xy_theta
return
# Modify particles using delta and inject noise.
for particle in self.particle_cloud:
# Step 1: turn particles in direction of translation
# Compute the unit vector of the desired heading to move in
heading_mag = math.sqrt(delta[0]**2 + delta[1]**2)
heading_uv = np.array(
[delta[0] / heading_mag, delta[1] / heading_mag])
# Compute the unit vector of the robot's current heading
robot_uv = np.array([
np.cos(self.current_odom_xy_theta[2]),
np.sin(self.current_odom_xy_theta[2])
])
# Calculate the angle r_1 that is between the current heading and target heading
r_1 = np.arccos(np.dot(robot_uv, heading_uv))
particle.theta += r_1 + np.random.normal(scale=.05)
# Step 2: move particles forward distance of translation
d = math.sqrt(delta[0]**2 +
delta[1]**2) + np.random.normal(scale=.05)
# Decompose the translation vector into x and y componenets
particle.x += d * np.cos(particle.theta)
particle.y += d * np.sin(particle.theta)
# Step 3: turn particles to final angle
r_2 = delta[2] - r_1
particle.theta += r_2
def map_calc_range(self, x, y, theta):
""" Difficulty Level 3: implement a ray tracing likelihood model... Let me know if you are interested """
# TODO: nothing unless you want to try this alternate likelihood model
pass
def resample_particles(self):
""" Resample the particles according to the new particle weights.
The weights stored with each particle should define the probability that a particular
particle is selected in the resampling step. You may want to make use of the given helper
function draw_random_sample.
"""
# make sure the distribution is normalized
self.normalize_particles()
weights = []
for particle in self.particle_cloud:
weights.append(particle.w)
choices = self.draw_random_sample(self.particle_cloud, weights,
self.n_particles)
# Reset particle cloud
self.particle_cloud = []
# Populate particle cloud with sampled choices
for chosen_particle in choices:
self.particle_cloud.append(
Particle(chosen_particle.x, chosen_particle.y,
chosen_particle.theta, chosen_particle.w))
def update_particles_with_laser(self, msg):
""" Updates the particle weights in response to the scan contained in the msg """
lidar_scan_angles = range(360)
# Populates lidar_scan list with (theta, distance) for each lidar scan angle
lidar_scan = []
for theta in lidar_scan_angles:
distance = msg.ranges[theta]
point = (theta, distance)
lidar_scan.append(point)
# Calculates the probability that each particle is the best estimate for the robot location
self.scan_probabilities = []
for p in self.particle_cloud:
particle_theta_prob = []
for point in lidar_scan:
x_vector = p.x + point[1] * math.cos(
math.radians(point[0]) + p.theta)
y_vector = p.y + point[1] * math.sin(
math.radians(point[0]) + p.theta)
closest_object = self.occupancy_field.get_closest_obstacle_distance(
x_vector, y_vector)
# Calculate probabilities using a tuned function
particle_theta_prob.append(1 / ((0.1 * closest_object)**2 + 1))
# Combine probability at every theta for every particle
self.scan_probabilities.append(
reduce(lambda a, b: a * b, particle_theta_prob))
@staticmethod
def draw_random_sample(choices, probabilities, n):
""" Return a random sample of n elements from the set choices with the specified probabilities
choices: the values to sample from represented as a list
probabilities: the probability of selecting each element in choices represented as a list
n: the number of samples
"""
values = np.array(range(len(choices)))
probs = np.array(probabilities)
bins = np.add.accumulate(probs)
inds = values[np.digitize(random_sample(n), bins)]
samples = []
for i in inds:
samples.append(deepcopy(choices[int(i)]))
return samples
def update_initial_pose(self, msg):
""" Callback function to handle re-initializing the particle filter based on a pose estimate.
These pose estimates could be generated by another ROS Node or could come from the rviz GUI """
xy_theta = self.transform_helper.convert_pose_to_xy_and_theta(
msg.pose.pose)
self.initialize_particle_cloud(msg.header.stamp, xy_theta)
def initialize_particle_cloud(self, timestamp, xy_theta=None):
""" Initialize the particle cloud.
Arguments
xy_theta: a triple consisting of the mean x, y, and theta (yaw) to initialize the
particle cloud around. If this input is omitted, the odometry will be used """
if xy_theta is None:
xy_theta = self.transform_helper.convert_pose_to_xy_and_theta(
self.odom_pose.pose)
# Create particles based on gaussian distribution centered around xy_theta
self.particle_cloud = []
for g in range(self.n_particles):
x = np.random.normal(xy_theta[0], scale=0.3)
y = np.random.normal(xy_theta[1], scale=0.3)
theta = np.random.normal(xy_theta[2], scale=0.1)
self.particle_cloud.append(Particle(x, y, theta, 1))
self.normalize_particles()
self.update_robot_pose(timestamp)
def normalize_particles(self):
""" Make sure the particle weights define a valid distribution (i.e. sum to 1.0) """
# Check if scan probabilities has been populated for each particle
if len(self.scan_probabilities) == len(self.particle_cloud):
sum_of_prob = sum(self.scan_probabilities)
for i, particle in enumerate(self.particle_cloud):
particle.w = self.scan_probabilities[i] / sum_of_prob
def publish_particles(self, msg):
particles_conv = []
for p in self.particle_cloud:
particles_conv.append(p.as_pose())
# actually send the message so that we can view it in rviz
self.particle_pub.publish(
PoseArray(header=Header(stamp=rospy.Time.now(),
frame_id=self.map_frame),
poses=particles_conv))
def publish_weights(self, msg):
# Visualize particle weights in rviz to get a better debug each particle
weight_markers = MarkerArray()
for i, particle in enumerate(self.particle_cloud):
weight_markers.markers.append(particle.as_marker(i))
self.weight_pub.publish(weight_markers)
def scan_received(self, msg):
""" This is the default logic for what to do when processing scan data.
Feel free to modify this, however, we hope it will provide a good
guide. The input msg is an object of type sensor_msgs/LaserScan """
if not (self.initialized):
# wait for initialization to complete
return
# wait a little while to see if the transform becomes available. This fixes a race
# condition where the scan would arrive a little bit before the odom to base_link transform
# was updated.
self.tf_listener.waitForTransform(self.base_frame, msg.header.frame_id,
msg.header.stamp,
rospy.Duration(0.5))
if not (self.tf_listener.canTransform(
self.base_frame, msg.header.frame_id, msg.header.stamp)):
# need to know how to transform the laser to the base frame
# this will be given by either Gazebo or neato_node
return
if not (self.tf_listener.canTransform(self.base_frame, self.odom_frame,
msg.header.stamp)):
# need to know how to transform between base and odometric frames
# this will eventually be published by either Gazebo or neato_node
return
# calculate pose of laser relative to the robot base
p = PoseStamped(
header=Header(stamp=rospy.Time(0), frame_id=msg.header.frame_id))
self.laser_pose = self.tf_listener.transformPose(self.base_frame, p)
# find out where the robot thinks it is based on its odometry
p = PoseStamped(header=Header(stamp=msg.header.stamp,
frame_id=self.base_frame),
pose=Pose())
self.odom_pose = self.tf_listener.transformPose(self.odom_frame, p)
# store the the odometry pose in a more convenient format (x,y,theta)
new_odom_xy_theta = self.transform_helper.convert_pose_to_xy_and_theta(
self.odom_pose.pose)
if not self.current_odom_xy_theta:
self.current_odom_xy_theta = new_odom_xy_theta
return
if not (self.particle_cloud):
# now that we have all of the necessary transforms we can update the particle cloud
self.initialize_particle_cloud(msg.header.stamp)
elif (math.fabs(new_odom_xy_theta[0] - self.current_odom_xy_theta[0]) >
self.d_thresh
or math.fabs(new_odom_xy_theta[1] -
self.current_odom_xy_theta[1]) > self.d_thresh
or math.fabs(new_odom_xy_theta[2] -
self.current_odom_xy_theta[2]) > self.a_thresh):
# we have moved far enough to do an update!
self.update_particles_with_odom(msg) # update based on odometry
if self.last_projected_stable_scan:
last_projected_scan_timeshift = deepcopy(
self.last_projected_stable_scan)
last_projected_scan_timeshift.header.stamp = msg.header.stamp
self.scan_in_base_link = self.tf_listener.transformPointCloud(
"base_link", last_projected_scan_timeshift)
self.update_particles_with_laser(msg) # update based on laser scan
self.update_robot_pose(msg.header.stamp) # update robot's pose
self.resample_particles(
) # resample particles to focus on areas of high density
# publish particles (so things like rviz can see them)
self.publish_particles(msg)
self.publish_weights(msg)
class ParticleFilter:
""" The class that represents a Particle Filter ROS Node
Attributes list:
initialized: a Boolean flag to communicate to other class methods that initializaiton is complete
base_frame: the name of the robot base coordinate frame (should be "base_link" for most robots)
map_frame: the name of the map coordinate frame (should be "map" in most cases)
odom_frame: the name of the odometry coordinate frame (should be "odom" in most cases)
scan_topic: the name of the scan topic to listen to (should be "scan" in most cases)
n_particles: the number of particles in the filter
d_thresh: the amount of linear movement before triggering a filter update
a_thresh: the amount of angular movement before triggering a filter update
laser_max_distance: the maximum distance to an obstacle we should use in a likelihood calculation
pose_listener: a subscriber that listens for new approximate pose estimates (i.e. generated through the rviz GUI)
particle_pub: a publisher for the particle cloud
laser_subscriber: listens for new scan data on topic self.scan_topic
tf_listener: listener for coordinate transforms
tf_broadcaster: broadcaster for coordinate transforms
particle_cloud: a list of particles representing a probability distribution over robot poses
current_odom_xy_theta: the pose of the robot in the odometry frame when the last filter update was performed.
The pose is expressed as a list [x,y,theta] (where theta is the yaw)
map: the map we will be localizing ourselves in. The map should be of type nav_msgs/OccupancyGrid
"""
def __init__(self):
self.initialized = False # make sure we don't perform updates before everything is setup
rospy.init_node('pf') # tell roscore that we are creating a new node named "pf"
self.base_frame = "base_link" # the frame of the robot base
self.map_frame = "map" # the name of the map coordinate frame
self.odom_frame = "odom" # the name of the odometry coordinate frame
self.scan_topic = "scan" # the topic where we will get laser scans from
self.n_particles = 100 # the number of particles to use
self.d_thresh = 0.2 # the amount of linear movement before performing an update
self.a_thresh = math.pi/6 # the amount of angular movement before performing an update
self.laser_max_distance = 2.0 # maximum penalty to assess in the likelihood field model
self.sigma = 0.08
# TODO: define additional constants if needed
# Setup pubs and subs
# pose_listener responds to selection of a new approximate robot location (for instance using rviz)
self.pose_listener = rospy.Subscriber("initialpose", PoseWithCovarianceStamped, self.update_initial_pose)
# publish the current particle cloud. This enables viewing particles in rviz.
self.particle_pub = rospy.Publisher("particlecloud", PoseArray, queue_size=10)
self.marker_pub = rospy.Publisher("markers", MarkerArray, queue_size=10)
# laser_subscriber listens for data from the lidar
# Dados do Laser: Mapa de verossimilhança/Occupancy field/Likehood map e Traçado de raios.
# Traçado de raios: Leitura em ângulo que devolve distância, através do sensor. Dado o mapa,
# extender a direção da distância pra achar um obstáculo.
self.laser_subscriber = rospy.Subscriber(self.scan_topic, LaserScan, self.scan_received)
# enable listening for and broadcasting coordinate transforms
#atualização de posição(odometria)
self.tf_listener = TransformListener()
self.tf_broadcaster = TransformBroadcaster()
self.particle_cloud = []
self.current_odom_xy_theta = []
#Chamar o mapa a partir de funcao externa
mapa = chama_mapa.obter_mapa()
# request the map from the map server, the map should be of type nav_msgs/OccupancyGrid
# TODO: fill in the appropriate service call here. The resultant map should be assigned be passed
# into the init method for OccupancyField
# for now we have commented out the occupancy field initialization until you can successfully fetch the map
self.occupancy_field = OccupancyField(mapa)
self.initialized = True
def update_robot_pose(self):
print("Update Robot Pose")
""" Update the estimate of the robot's pose given the updated particles.
There are two logical methods for this:
(1): compute the mean pose
(2): compute the most likely pose (i.e. the mode of the distribution)
"""
# first make sure that the particle weights are normalized
self.normalize_particles()
# TODO: assign the lastest pose into self.robot_pose as a geometry_msgs.Pose object
# just to get started we will fix the robot's pose to always be at the origin
#Variaveis para soma do X,Y e angulo Theta da particula
x_sum = 0
y_sum = 0
theta_sum = 0
#Loop de soma para as variaveis relativas a probabilidade da particula
for p in self.particle_cloud:
x_sum += p.x * p.w
y_sum += p.y * p.w
theta_sum += p.theta * p.w
#Atributo robot_pose eh o pose da melhor particula possivel a partir das variaveis de soma
self.robot_pose = Particle(x=x_sum, y=y_sum, theta=theta_sum).as_pose()
def update_particles_with_odom(self,msg):
print("Update Particles with Odom")
""" Update the particles using the newly given odometry pose.
The function computes the value delta which is a tuple (x,y,theta)
that indicates the change in position and angle between the odometry
when the particles were last updated and the current odometry.
msg: this is not really needed to implement this, but is here just in case.
"""
new_odom_xy_theta = convert_pose_to_xy_and_theta(self.odom_pose.pose)
# compute the change in x,y,theta since our last update
if self.current_odom_xy_theta:
old_odom_xy_theta = self.current_odom_xy_theta
delta = (new_odom_xy_theta[0] - self.current_odom_xy_theta[0],
new_odom_xy_theta[1] - self.current_odom_xy_theta[1],
new_odom_xy_theta[2] - self.current_odom_xy_theta[2])
self.current_odom_xy_theta = new_odom_xy_theta
else:
self.current_odom_xy_theta = new_odom_xy_theta
#R eh o raio feito a partir de um pitagoras com o X e Y da variacao Delta
r = math.sqrt(delta[0]**2 + delta[1]**2)
#Funcao para conseguir um valor entre -pi e pi e subtrair o antigo valor de theta. (Achei um pouco confusa, )
phi = math.atan2(delta[1], delta[0])-old_odom_xy_theta[2]
for particle in self.particle_cloud:
particle.x += r*math.cos(phi+particle.theta)
particle.y += r*math.sin(phi+particle.theta)
particle.theta += delta[2]
# TODO: modify particles using delta
# For added difficulty: Implement sample_motion_odometry (Prob Rob p 136)
def map_calc_range(self,x,y,theta):
""" Difficulty Level 3: implement a ray tracing likelihood model... Let me know if you are interested """
# TODO: nothing unless you want to try this alternate likelihood model
pass
def resample_particles(self):
""" Resample the particles according to the new particle weights.
The weights stored with each particle should define the probability that a particular
particle is selected in the resampling step. You may want to make use of the given helper
function draw_random_sample.
"""
#Primeiro de tudo, normalizar particulas
self.normalize_particles()
#Criar array do numpy vazia do tamanho do numero de particulas.
values = np.empty(self.n_particles)
#Preencher essa lista com os indices das particulas
for i in range(self.n_particles):
values[i] = i
#Criar uma lista para novas particulas
new_particles = []
#Criar lista com os indices das particulas com mais probabilidade
random_particles = ParticleFilter.weighted_values(values,[p.w for p in self.particle_cloud],self.n_particles)
for i in random_particles:
#Transformar o I em inteiro para corrigir bug de float
int_i = int(i)
#Pegar particula na possicao I na nuvem de particulas.
p = self.particle_cloud[int_i]
#Adicionar particulas somando um valor aleatorio da distribuicao gauss com media = 0 e desvio padrao = 0.025
new_particles.append(Particle(x=p.x+gauss(0,.025),y=p.y+gauss(0,.025),theta=p.theta+gauss(0,.025)))
#Igualar nuvem de particulas a novo sample criado
self.particle_cloud = new_particles
#Normalizar mais uma vez as particulas.
self.normalize_particles()
def update_particles_with_laser(self, msg):
print("Update Particles with laser")
""" Updates the particle weights in response to the scan contained in the msg """
for p in self.particle_cloud:
for i in range(360):
#Distancia no angulo I
distancia = msg.ranges[i]
x = distancia * math.cos(i + p.theta)
y = distancia * math.sin(i + p.theta)
#Verificar se distancia minima eh diferente de nan
erro_nan = self.occupancy_field.get_closest_obstacle_distance(x,y)
if erro_nan is not float('nan'):
# Adicionar valor para corrigir erro de nan (Retirado de outro codigo)
p.w += math.exp(-erro_nan*erro_nan/(2*self.sigma**2))
#Normalizar particulas e criar um novo sample das mesmas
self.normalize_particles()
self.resample_particles()
@staticmethod
def weighted_values(values, probabilities, size):
""" Return a random sample of size elements from the set values with the specified probabilities
values: the values to sample from (numpy.ndarray)
probabilities: the probability of selecting each element in values (numpy.ndarray)
size: the number of samples
"""
bins = np.add.accumulate(probabilities)
return values[np.digitize(random_sample(size), bins)]
@staticmethod
#Nao estou usando esse metodo. Apenas o weighted_values
def draw_random_sample(choices, probabilities, n):
print("Draw Random Sample")
""" Return a random sample of n elements from the set choices with the specified probabilities
choices: the values to sample from represented as a list
probabilities: the probability of selecting each element in choices represented as a list
n: the number of samples
"""
values = np.array(range(len(choices)))
probs = np.array(probabilities)
bins = np.add.accumulate(probs)
inds = values[np.digitize(random_sample(n), bins)]
samples = []
for i in inds:
samples.append(deepcopy(choices[int(i)]))
return samples
def update_initial_pose(self, msg):
print("Update Initial Pose")
""" Callback function to handle re-initializing the particle filter based on a pose estimate.
These pose estimates could be generated by another ROS Node or could come from the rviz GUI """
xy_theta = convert_pose_to_xy_and_theta(msg.pose.pose)
self.initialize_particle_cloud(xy_theta)
self.fix_map_to_odom_transform(msg)
def initialize_particle_cloud(self, xy_theta=None):
""" Initialize the particle cloud.
Arguments
xy_theta: a triple consisting of the mean x, y, and theta (yaw) to initialize the
particle cloud around. If this input is ommitted, the odometry will be used """
if xy_theta == None:
xy_theta = convert_pose_to_xy_and_theta(self.odom_pose.pose)
self.particle_cloud = []
# TODO create particles
# Criando particula
print("Inicializacao da Cloud de Particulas")
#Valor auxiliar para nao dar erro na hora de criacao das particulas. Irrelevante
valor_aux = 0.3
for i in range (self.n_particles):
self.particle_cloud.append(Particle(0, 0, 0, valor_aux))
# Randomizar particulas em volta do robo.
for i in self.particle_cloud:
i.x = xy_theta[0]+ random.uniform(-1,1)
i.y = xy_theta[1]+ random.uniform(-1,1)
i.theta = xy_theta[2]+ random.uniform(-45,45)
#Normalizar as particulas e dar update na posicao do robo
self.normalize_particles()
self.update_robot_pose()
print(xy_theta)
def normalize_particles(self):
""" Make sure the particle weights define a valid distribution (i.e. sum to 1.0) """
#Soma total das probabilidades das particulas
w_sum = sum([p.w for p in self.particle_cloud])
#Dividir cada probabilidade de uma particula pela soma total
for particle in self.particle_cloud:
particle.w /= w_sum
# TODO: implement this
def publish_particles(self, msg):
print("Publicar Particulas.")
print(len(self.particle_cloud))
particles_conv = []
for p in self.particle_cloud:
particles_conv.append(p.as_pose())
# actually send the message so that we can view it in rviz
self.particle_pub.publish(PoseArray(header=Header(stamp=rospy.Time.now(),
frame_id=self.map_frame),
poses=particles_conv))
def scan_received(self, msg):
""" This is the default logic for what to do when processing scan data.
Feel free to modify this, however, I hope it will provide a good
guide. The input msg is an object of type sensor_msgs/LaserScan """
if not(self.initialized):
print("Not Initialized")
# wait for initialization to complete
return
if not(self.tf_listener.canTransform(self.base_frame,msg.header.frame_id,rospy.Time(0))):
print("Not 2")
# need to know how to transform the laser to the base frame
# this will be given by either Gazebo or neato_node
return
if not(self.tf_listener.canTransform(self.base_frame,self.odom_frame,rospy.Time(0))):
print("Not 3")
# need to know how to transform between base and odometric frames
# this will eventually be published by either Gazebo or neato_node
return
# calculate pose of laser relative ot the robot base
p = PoseStamped(header=Header(stamp=rospy.Time(0),
frame_id=msg.header.frame_id))
self.laser_pose = self.tf_listener.transformPose(self.base_frame,p)
# find out where the robot thinks it is based on its odometry
p = PoseStamped(header=Header(stamp = rospy.Time(0),
frame_id=self.base_frame),
pose=Pose())
self.odom_pose = self.tf_listener.transformPose(self.odom_frame, p)
# store the the odometry pose in a more convenient format (x,y,theta)
new_odom_xy_theta = convert_pose_to_xy_and_theta(self.odom_pose.pose)
print("PASSOU")
if not(self.particle_cloud):
# now that we have all of the necessary transforms we can update the particle cloud
self.initialize_particle_cloud()
# cache the last odometric pose so we can only update our particle filter if we move more than self.d_thresh or self.a_thresh
self.current_odom_xy_theta = new_odom_xy_theta
# update our map to odom transform now that the particles are initialized
self.fix_map_to_odom_transform(msg)
elif (math.fabs(new_odom_xy_theta[0] - self.current_odom_xy_theta[0]) > self.d_thresh or
math.fabs(new_odom_xy_theta[1] - self.current_odom_xy_theta[1]) > self.d_thresh or
math.fabs(new_odom_xy_theta[2] - self.current_odom_xy_theta[2]) > self.a_thresh):
# we have moved far enough to do an update!
self.update_particles_with_odom(msg) # update based on odometry
self.update_particles_with_laser(msg) # update based on laser scan
self.update_robot_pose() # update robot's pose
self.resample_particles() # resample particles to focus on areas of high density
self.fix_map_to_odom_transform(msg) # update map to odom transform now that we have new particles
# publish particles (so things like rviz can see them)
self.publish_particles(msg)
# direcionar particulas quando um obstaculo é identificado.
def fix_map_to_odom_transform(self, msg):
print("Fix Map to Odom Transform")
""" This method constantly updates the offset of the map and
odometry coordinate systems based on the latest results from
the localizer """
(translation, rotation) = convert_pose_inverse_transform(self.robot_pose)
p = PoseStamped(pose=convert_translation_rotation_to_pose(translation,rotation),
header=Header(stamp=rospy.Time(0),frame_id=self.base_frame))
self.odom_to_map = self.tf_listener.transformPose(self.odom_frame, p)
(self.translation, self.rotation) = convert_pose_inverse_transform(self.odom_to_map.pose)
def broadcast_last_transform(self):
print("Broadcast")
""" Make sure that we are always broadcasting the last map
to odom transformation. This is necessary so things like
move_base can work properly. """
if not(hasattr(self,'translation') and hasattr(self,'rotation')):
return
self.tf_broadcaster.sendTransform(self.translation,
self.rotation,
rospy.get_rostime(),
self.odom_frame,
self.map_frame)
class Generate_Point():
def __init__(self, point_num, delta, radius=1, deltaX=0, deltaY=0):
rospy.init_node('demo', anonymous=True)
self.worldFrame = rospy.get_param("~worldFrame", "/world")
self.frame = rospy.get_param("~frame")
self.pubGoal = rospy.Publisher('goal', PoseStamped, queue_size=1)
self.listener = TransformListener()
rospy.Subscriber("cmd_vel", Twist, self.cmdVelCallback)
self.delta = delta
self.point_num = point_num
self.takeoffFlag = 0
self.radius = radius
self.deltaX = deltaX
self.deltaY = deltaY
self.goalIndex = 0
rospy.loginfo("demo start!!!!!!!")
def cmdVelCallback(self, data):
if data.linear.z != 0.0 and self.takeoffFlag == 0:
self.takeoffFlag = 1
rospy.sleep(10)
self.takeoffFlag = 2
def run(self):
self.listener.waitForTransform(self.worldFrame, self.frame,
rospy.Time(), rospy.Duration(10.0))
goal = PoseStamped()
goal.header.seq = 0
goal.header.frame_id = self.worldFrame
while not rospy.is_shutdown():
self.circle_generate(goal, self.goalIndex)
self.pubGoal.publish(goal)
t = self.listener.getLatestCommonTime(self.worldFrame, self.frame)
if self.listener.canTransform(self.worldFrame, self.frame, t):
position, quaternion = self.listener.lookupTransform(
self.worldFrame, self.frame, t)
rpy = tf.transformations.euler_from_quaternion(quaternion)
if self.takeoffFlag == 1:
self.goalIndex = 0
elif self.takeoffFlag == 2 and self.goalIndex < self.point_num - 1:
rospy.sleep(0.02)
rospy.loginfo(self.goalIndex)
self.goalIndex += 1
def circle_generate(self, goal, index):
circle = 5
point_num = self.point_num
delta = self.delta
radius = self.radius
offset_x = self.deltaX
offset_y = self.deltaY
angle = circle * index * 2 * math.pi / point_num + delta
x = radius * math.cos(angle) + offset_x
y = radius * math.sin(angle) + offset_y
goal.header.seq += 1
goal.header.stamp = rospy.Time.now()
goal.pose.position.x = x
goal.pose.position.y = y
goal.pose.position.z = 0.8
quaternion = tf.transformations.quaternion_from_euler(0, 0, 0)
goal.pose.orientation.x = quaternion[0]
goal.pose.orientation.y = quaternion[1]
goal.pose.orientation.z = quaternion[2]
goal.pose.orientation.w = quaternion[3]
class ORKTabletop(object):
""" Listens to the table and object messages from ORK. Provides ActionServer
that assembles table and object into same message.
NB - the table is an axis-aligned bounding box in the kinect's frame"""
def __init__(self, name):
self.sub = rospy.Subscriber("/recognized_object_array", RecognizedObjectArray, self.callback)
self.pub = rospy.Publisher('/recognized_object_array_as_point_cloud', PointCloud2)
self.pose_pub = rospy.Publisher('/recognized_object_array_as_pose_stamped', PoseStamped)
# We listen for ORK's MarkerArray of tables on this topic
self.table_topic = "/marker_tables"
self.tl = TransformListener()
# create messages that are used to publish feedback/result.
# accessed by multiple threads
self._result = TabletopResult()
self.result_lock = threading.Lock()
# used s.t. we don't return a _result message that hasn't been updated yet.
self.has_data = False
self._action_name = name
self._as = actionlib.SimpleActionServer(self._action_name, TabletopAction,
execute_cb=self.execute_cb, auto_start=False)
self._as.start()
# TODO: Is this really the best structure for handling the callbacks?
# Would it be possible to have separate callbacks for table and objects, each updating a most-recently-seen variable?
# Or maybe use the message_filters.TimeSynchronizer class if corresponding table/object data has identical timestamps?
def callback(self, data):
rospy.loginfo("Objects %d", data.objects.__len__())
table_corners = []
# obtain table_offset and table_pose
to = rospy.wait_for_message(self.table_topic, MarkerArray);
# obtain Table corners ...
rospy.loginfo("Tables hull size %d", to.markers.__len__())
if not to.markers:
rospy.loginfo("No tables detected")
return
else:
# NB - D says that ORK has this set to filter based on height.
# IIRC, it's 0.6-0.8m above whatever frame is set as the floor
point_array_size = 4 # for the 4 corners of the bounding box
for i in range (0, point_array_size):
p = Point()
p.x = to.markers[0].points[i].x
p.y = to.markers[0].points[i].y
p.z = to.markers[0].points[i].z
table_corners.append(p)
# this is a table pose at the edge close to the robot, in the center of x axis
table_pose = PoseStamped()
table_pose.header = to.markers[0].header
table_pose.pose = to.markers[0].pose
# Determine table dimensions
rospy.loginfo('calculating table pose bounding box in frame: %s' % table_pose.header.frame_id)
min_x = sys.float_info.max
min_y = sys.float_info.max
max_x = -sys.float_info.max
max_y = -sys.float_info.max
for i in range (table_corners.__len__()):
if table_corners[i].x > max_x:
max_x = table_corners[i].x
if table_corners[i].y > max_y:
max_y = table_corners[i].y
if table_corners[i].x < min_x:
min_x = table_corners[i].x
if table_corners[i].y < min_y:
min_y = table_corners[i].y
table_dim = Point()
# TODO: if we don't (can't!) compute the height, should we at least give it non-zero depth?
# (would also require shifting the observed centroid down by table_dim.z/2)
table_dim.z = 0.0
table_dim.x = abs(max_x - min_x)
table_dim.y = abs(max_y - min_y)
print "Dimensions: ", table_dim.x, table_dim.y
# Temporary frame used for transformations
table_link = 'table_link'
# centroid of a table in table_link frame
centroid = PoseStamped()
centroid.header.frame_id = table_link
centroid.header.stamp = table_pose.header.stamp
centroid.pose.position.x = (max_x + min_x)/2.
centroid.pose.position.y = (max_y + min_y)/2.
centroid.pose.position.z = 0.0
centroid.pose.orientation.x = 0.0
centroid.pose.orientation.y = 0.0
centroid.pose.orientation.z = 0.0
centroid.pose.orientation.w = 1.0
# generate transform from table_pose to our newly-defined table_link
tt = TransformStamped()
tt.header = table_pose.header
tt.child_frame_id = table_link
tt.transform.translation = table_pose.pose.position
tt.transform.rotation = table_pose.pose.orientation
self.tl.setTransform(tt)
self.tl.waitForTransform(table_link, table_pose.header.frame_id, table_pose.header.stamp, rospy.Duration(3.0))
if self.tl.canTransform(table_pose.header.frame_id, table_link, table_pose.header.stamp):
centroid_table_pose = self.tl.transformPose(table_pose.header.frame_id, centroid)
self.pose_pub.publish(centroid_table_pose)
else:
rospy.logwarn("No transform between %s and %s possible",table_pose.header.frame_id, table_link)
return
# transform each object into desired frame; add to list of clusters
cluster_list = []
for i in range (data.objects.__len__()):
rospy.loginfo("Point clouds %s", data.objects[i].point_clouds.__len__())
pc = PointCloud2()
pc = data.objects[i].point_clouds[0]
arr = pointclouds.pointcloud2_to_array(pc, 1)
arr_xyz = pointclouds.get_xyz_points(arr)
arr_xyz_trans = []
for j in range (arr_xyz.__len__()):
ps = PointStamped();
ps.header.frame_id = table_link
ps.header.stamp = table_pose.header.stamp
ps.point.x = arr_xyz[j][0]
ps.point.y = arr_xyz[j][1]
ps.point.z = arr_xyz[j][2]
if self.tl.canTransform(table_pose.header.frame_id, table_link, table_pose.header.stamp):
ps_in_kinect_frame = self.tl.transformPoint(table_pose.header.frame_id, ps)
else:
rospy.logwarn("No transform between %s and %s possible",table_pose.header.frame_id, table_link)
return
arr_xyz_trans.append([ps_in_kinect_frame.point.x, ps_in_kinect_frame.point.y, ps_in_kinect_frame.point.z])
pc_trans = PointCloud2()
pc_trans = pointclouds.xyz_array_to_pointcloud2(np.asarray([arr_xyz_trans]),
table_pose.header.stamp, table_pose.header.frame_id)
self.pub.publish(pc_trans)
cluster_list.append(pc_trans)
rospy.loginfo("cluster size %d", cluster_list.__len__())
# finally - save all data in the object that'll be sent in response to actionserver requests
with self.result_lock:
self._result.objects = cluster_list
self._result.table_dims = table_dim
self._result.table_pose = centroid_table_pose
self.has_data = True
def execute_cb(self, goal):
rospy.loginfo('Executing ORKTabletop action')
# want to get the NEXT data coming in, rather than the current one.
with self.result_lock:
self.has_data = False
rr = rospy.Rate(1.0)
while not rospy.is_shutdown() and not self._as.is_preempt_requested():
with self.result_lock:
if self.has_data:
break
rr.sleep()
if self._as.is_preempt_requested():
rospy.loginfo('%s: Preempted' % self._action_name)
self._as.set_preempted()
elif rospy.is_shutdown():
self._as.set_aborted()
else:
with self.result_lock:
rospy.loginfo('%s: Succeeded' % self._action_name)
self._as.set_succeeded(self._result)
class Figure8():
def __init__(self, goals):
rospy.init_node('figure8', anonymous=True)
self.worldFrame = rospy.get_param("~worldFrame", "/world")
self.frame = rospy.get_param("~frame")
self.radius = rospy.get_param("~radius")
self.freq = rospy.get_param("~freq")
self.lap = rospy.get_param("~lap")
self.pubGoal = rospy.Publisher('goal', PoseStamped, queue_size=1)
self.listener = TransformListener()
self.goals = goals
self.goalIndex = 0
def run(self):
self.listener.waitForTransform(self.worldFrame, self.frame, rospy.Time(), rospy.Duration(5.0))
#rospy.loginfo("start running!")
goal = PoseStamped()
goal.header.seq = 0
goal.header.frame_id = self.worldFrame
while not rospy.is_shutdown():
#rospy.loginfo("start running!")
goal.header.seq += 1
goal.header.stamp = rospy.Time.now()
goal.pose.position.x = self.goals[self.goalIndex][0]
goal.pose.position.y = self.goals[self.goalIndex][1]
goal.pose.position.z = self.goals[self.goalIndex][2]
quaternion = tf.transformations.quaternion_from_euler(0, 0, self.goals[self.goalIndex][3])
goal.pose.orientation.x = quaternion[0]
goal.pose.orientation.y = quaternion[1]
goal.pose.orientation.z = quaternion[2]
goal.pose.orientation.w = quaternion[3]
self.pubGoal.publish(goal)
t = self.listener.getLatestCommonTime(self.worldFrame, self.frame)
if self.listener.canTransform(self.worldFrame, self.frame, t):
position, quaternion = self.listener.lookupTransform(self.worldFrame, self.frame, t)
rpy = tf.transformations.euler_from_quaternion(quaternion)
if math.fabs(position[0] - self.goals[self.goalIndex][0]) < 0.15 \
and math.fabs(position[1] - self.goals[self.goalIndex][1]) < 0.15 \
and math.fabs(position[2] - self.goals[self.goalIndex][2]) < 0.15 \
and math.fabs(rpy[2] - self.goals[self.goalIndex][3]) < math.radians(10) \
and self.goalIndex < len(self.goals) - 2:
rospy.sleep(self.goals[self.goalIndex][4])
self.goalIndex += 1
rospy.loginfo("Index:%lf",self.goalIndex)
if self.goalIndex == len(self.goals) - 2:
break
t_start= rospy.Time.now().to_sec()
#rospy.loginfo("t_start:%lf",t_start)
t_now=t_start
while ((not rospy.is_shutdown()) and (t_now-t_start<self.lap/self.freq)):
goal.header.seq += 1
goal.header.stamp = rospy.Time.now()
goal.pose.position.x = self.goals[self.goalIndex-1][0]+self.radius*math.sin((t_now-t_start)*2*math.pi*2*self.freq)
goal.pose.position.y = self.goals[self.goalIndex-1][1]+2*self.radius*math.sin((t_now-t_start)*2*math.pi*self.freq)
goal.pose.position.z = self.goals[self.goalIndex-1][2]
quaternion = tf.transformations.quaternion_from_euler(0, 0, self.goals[self.goalIndex-1][3])
goal.pose.orientation.x = quaternion[0]
goal.pose.orientation.y = quaternion[1]
goal.pose.orientation.z = quaternion[2]
goal.pose.orientation.w = quaternion[3]
self.pubGoal.publish(goal)
t_now= rospy.Time.now().to_sec()
rospy.loginfo("t_now-t_start:%lf",t_now-t_start)
while not rospy.is_shutdown():
goal.header.seq += 1
goal.header.stamp = rospy.Time.now()
goal.pose.position.x = self.goals[self.goalIndex][0]
goal.pose.position.y = self.goals[self.goalIndex][1]
goal.pose.position.z = self.goals[self.goalIndex][2]
quaternion = tf.transformations.quaternion_from_euler(0, 0, self.goals[self.goalIndex][3])
goal.pose.orientation.x = quaternion[0]
goal.pose.orientation.y = quaternion[1]
goal.pose.orientation.z = quaternion[2]
goal.pose.orientation.w = quaternion[3]
self.pubGoal.publish(goal)
#rospy.loginfo("Index:%lf",self.goalIndex)
t = self.listener.getLatestCommonTime(self.worldFrame, self.frame)
if self.listener.canTransform(self.worldFrame, self.frame, t):
position, quaternion = self.listener.lookupTransform(self.worldFrame, self.frame, t)
rpy = tf.transformations.euler_from_quaternion(quaternion)
if math.fabs(position[0] - self.goals[self.goalIndex][0]) < 0.15 \
and math.fabs(position[1] - self.goals[self.goalIndex][1]) < 0.15 \
and math.fabs(position[2] - self.goals[self.goalIndex][2]) < 0.15 \
and math.fabs(rpy[2] - self.goals[self.goalIndex][3]) < math.radians(10) \
and self.goalIndex < len(self.goals) - 1:
rospy.sleep(self.goals[self.goalIndex][4])
self.goalIndex += 1
class ParticleFilter:
""" The class that represents a Particle Filter ROS Node
Attributes list:
initialized: a Boolean flag to communicate to other class methods that initializaiton is complete
base_frame: the name of the robot base coordinate frame (should be "base_link" for most robots)
map_frame: the name of the map coordinate frame (should be "map" in most cases)
odom_frame: the name of the odometry coordinate frame (should be "odom" in most cases)
scan_topic: the name of the scan topic to listen to (should be "scan" in most cases)
n_particles: the number of particles in the filter
linear_mov: the amount of linear movement before triggering a filter update
angular_mov: the amount of angular movement before triggering a filter update
laser_max_distance: the maximum distance to an obstacle we should use in a likelihood calculation
pose_listener: a subscriber that listens for new approximate pose estimates (i.e. generated through the rviz GUI)
particle_pub: a publisher for the particle cloud
laser_subscriber: listens for new scan data on topic self.scan_topic
tf_listener: listener for coordinate transforms
particle_cloud: a list of particles representing a probability distribution over robot poses
current_odom_xy_theta: the pose of the robot in the odometry frame when the last filter update was performed.
The pose is expressed as a list [x,y,theta] (where theta is the yaw)
map: the map we will be localizing ourselves in. The map should be of type nav_msgs/OccupancyGrid
"""
def __init__(self):
self.initialized = False # make sure we don't perform updates before everything is setup
rospy.init_node('RMI_pf')
self.base_frame = "base_link" # the frame of the robot base
self.map_frame = "map" # the name of the map coordinate frame
self.odom_frame = "odom" # the name of the odometry coordinate frame
self.scan_topic = "scan" # the topic where we will get laser scans from
self.n_particles = 20
self.linear_mov = 0.1
self.angular_mov = math.pi / 10
self.laser_max_distance = 2.0
self.sigma = 0.05
# Descomentar essa linha caso /initialpose seja publicada
# self.pose_listener = rospy.Subscriber("initialpose",
# PoseWithCovarianceStamped,
# self.update_initial_pose)
self.laser_subscriber = rospy.Subscriber(self.scan_topic, LaserScan,
self.scan_received)
self.particle_pub = rospy.Publisher("particlecloud_rmi",
PoseArray,
queue_size=1)
self.tf_listener = TransformListener()
self.particle_cloud = []
self.current_odom_xy_theta = []
self.map_server = rospy.ServiceProxy('static_map', GetMap)
self.map = self.map_server().map
self.occupancy_field = OccupancyField(self.map)
self.tf_listener.waitForTransform(self.odom_frame, self.base_frame,
rospy.Time(), rospy.Duration(1.0))
self.initialized = True
def update_particles_with_odom(self, msg):
new_odom_xy_theta = convert_pose_to_xy_and_theta(self.odom_pose.pose)
# print 'new_odom_xy_theta', new_odom_xy_theta
# Pega a posicao da odom (x,y,tehta)
if self.current_odom_xy_theta:
old_odom_xy_theta = self.current_odom_xy_theta
delta = (new_odom_xy_theta[0] - self.current_odom_xy_theta[0],
new_odom_xy_theta[1] - self.current_odom_xy_theta[1],
new_odom_xy_theta[2] - self.current_odom_xy_theta[2])
self.current_odom_xy_theta = new_odom_xy_theta
# print 'delta', delta
else:
self.current_odom_xy_theta = new_odom_xy_theta
return
for particle in self.particle_cloud:
d = math.sqrt((delta[0]**2) + (delta[1]**2))
# print 'particle_theta_1', particle.theta
particle.x += d * (math.cos(particle.theta) + normal(0, 0.01))
particle.y += d * (math.sin(particle.theta) + normal(0, 0.01))
particle.theta = self.current_odom_xy_theta[2] #+ normal(0,0.05)
# Systematic Resample
def resample_particles(self):
self.normalize_particles()
# for particle in self.particle_cloud:
# print 'TODAS PART', particle.w, particle.x, particle.y
weights = []
for particle in self.particle_cloud:
weights.append(particle.w)
newParticles = []
N = len(weights)
positions = (np.arange(N) + random.random()) / N
cumulative_sum = np.cumsum(weights)
i, j = 0, 0
while i < N:
if positions[i] < cumulative_sum[j]:
newParticles.append(deepcopy(self.particle_cloud[j]))
i += 1
else:
j += 1
self.particle_cloud = newParticles
def update_particles_with_laser(self, msg):
depths = []
for dist in msg.ranges:
if not np.isnan(dist):
depths.append(dist)
fullDepthsArray = msg.ranges[:]
# Verifica se ha objetos proximos ao robot
if len(depths) == 0:
self.closest = 0
self.position = 0
else:
self.closest = min(depths)
self.position = fullDepthsArray.index(self.closest)
# print 'self.position, self.closest', self.position, self.closest, self.xy_theta_aux
# print msg, '/scan'
for index, particle in enumerate(self.particle_cloud):
tot_prob = 0.0
for index, scan in enumerate(depths):
x, y = self.transform_scan(particle, scan, index)
# print 'x,y, scan', x, y, scan
# usa o metodo get_closest_obstacle_distance para buscar o obstaculo mais proximo dentro do range x,y do grid map
d = self.occupancy_field.get_closest_obstacle_distance(x, y)
# quanto mais proximo de zero mais relevante
tot_prob += math.exp((-d**2) / (2 * self.sigma**2))
tot_prob = tot_prob / len(depths)
if math.isnan(tot_prob):
particle.w = 1.0
else:
particle.w = tot_prob
# print 'LASER', particle.x, particle.y, particle.w
def transform_scan(self, particle, distance, theta):
return (particle.x +
distance * math.cos(math.radians(particle.theta + theta)),
particle.y +
distance * math.sin(math.radians(particle.theta + theta)))
def update_initial_pose(self, msg):
xy_theta = convert_pose_to_xy_and_theta(msg.pose.pose)
self.initialize_particle_cloud(xy_theta)
def initialize_particle_cloud(self, xy_theta=None):
print 'Cria o set inicial de particulas'
if xy_theta == None:
xy_theta = convert_pose_to_xy_and_theta(self.odom_pose.pose)
x, y, theta = xy_theta
# Altere este parametro para aumentar a circunferencia do filtro de particulas
# Na VM ate 1 e suportado
rad = 0.5
self.particle_cloud = []
self.particle_cloud.append(
Particle(xy_theta[0], xy_theta[1], xy_theta[2]))
# print 'particle_values_W', self.particle_cloud[0].w
# print 'particle_values_X', self.particle_cloud[0].x
# print 'particle_values_Y', self.particle_cloud[0].y
# print 'particle_values_THETA', self.particle_cloud[0].theta
for i in range(self.n_particles - 1):
# initial facing of the particle
theta = random.random() * 360
# compute params to generate x,y in a circle
other_theta = random.random() * 360
radius = random.random() * rad
# x => straight ahead
x = radius * math.sin(other_theta) + xy_theta[0]
y = radius * math.cos(other_theta) + xy_theta[1]
particle = Particle(x, y, theta)
self.particle_cloud.append(particle)
self.normalize_particles()
def normalize_particles(self):
tot_weight = sum([particle.w
for particle in self.particle_cloud]) or 1.0
for particle in self.particle_cloud:
particle.w = particle.w / tot_weight
def publish_particles(self, msg):
particles_conv = []
for p in self.particle_cloud:
particles_conv.append(p.as_pose(
)) # transforma a particula em POSE para ser entendida pelo ROS
# print 'PARTII', [particles.x for particles in self.particle_cloud]
# Publica as particulas no rviz (particloud_rmi)
self.particle_pub.publish(
PoseArray(header=Header(stamp=rospy.Time.now(),
frame_id=self.map_frame),
poses=particles_conv))
def scan_received(self, msg):
# print msg
""" This is the default logic for what to do when processing scan data.
Feel free to modify this, however, I hope it will provide a good
guide. The input msg is an object of type sensor_msgs/LaserScan """
if not (self.initialized):
# wait for initialization to complete
return
if not (self.tf_listener.canTransform(
self.base_frame, msg.header.frame_id, msg.header.stamp)):
# need to know how to transform the laser to the base frame
# this will be given by either Gazebo or neato_node
return
if not (self.tf_listener.canTransform(self.base_frame, self.odom_frame,
msg.header.stamp)):
# need to know how to transform between base and odometric frames
# this will eventually be published by either Gazebo or neato_node
return
# print 'msg.header.frame_id', msg.header.frame_id
# calculate pose of laser relative ot the robot base
p = PoseStamped(
header=Header(stamp=rospy.Time(0), frame_id=msg.header.frame_id))
self.laser_pose = self.tf_listener.transformPose(self.base_frame, p)
# find out where the robot thinks it is based on its odometry
# listener.getLatestCommonTime("/base_link",object_pose_in.header.frame_id)
# p = PoseStamped(header=Header(stamp=msg.header.stamp,
p = PoseStamped(
header=Header(
stamp=self.tf_listener.getLatestCommonTime(
self.base_frame, self.map_frame),
# p = PoseStamped(header=Header(stamp=rospy.Time.now(),
frame_id=self.base_frame),
pose=Pose())
# p_aux = PoseStamped(header=Header(stamp=self.tf_listener.getLatestCommonTime("/base_link","/map"),
p_aux = PoseStamped(
header=Header(
stamp=self.tf_listener.getLatestCommonTime(
self.odom_frame, self.map_frame),
# p_aux = PoseStamped(header=Header(stamp=rospy.Time.now(),
frame_id=self.odom_frame),
pose=Pose())
odom_aux = self.tf_listener.transformPose(self.map_frame, p_aux)
odom_aux_xy_theta = convert_pose_to_xy_and_theta(odom_aux.pose)
# print 'odom_aux_xy_theta', odom_aux_xy_theta
self.odom_pose = self.tf_listener.transformPose(self.odom_frame, p)
# print 'self.odom_pose', self.odom_pose
# (trans, root) = self.tf_listener.lookupTransform(self.odom_frame, self.base_frame, rospy.Time(0))
# self.odom_pose = trans
# print trans, root
new_odom_xy_theta = convert_pose_to_xy_and_theta(self.odom_pose.pose)
# new_odom_xy_theta = convert_pose_to_xy_and_theta(self.laser_pose.pose)
xy_theta_aux = (new_odom_xy_theta[0] + odom_aux_xy_theta[0],
new_odom_xy_theta[1] + odom_aux_xy_theta[1],
new_odom_xy_theta[2])
self.xy_theta_aux = xy_theta_aux
if not (self.particle_cloud):
self.initialize_particle_cloud(xy_theta_aux)
self.current_odom_xy_theta = new_odom_xy_theta
elif (math.fabs(new_odom_xy_theta[0] - self.current_odom_xy_theta[0]) >
self.linear_mov
or math.fabs(new_odom_xy_theta[1] -
self.current_odom_xy_theta[1]) > self.linear_mov
or math.fabs(new_odom_xy_theta[2] -
self.current_odom_xy_theta[2]) > self.angular_mov):
self.update_particles_with_odom(msg)
self.update_particles_with_laser(msg)
self.resample_particles()
self.publish_particles(msg)
class ParticleFilter:
""" The class that represents a Particle Filter ROS Node
Attributes list:
initialized: a Boolean flag to communicate to other class methods that initializaiton is complete
base_frame: the name of the robot base coordinate frame (should be "base_link" for most robots)
map_frame: the name of the map coordinate frame (should be "map" in most cases)
odom_frame: the name of the odometry coordinate frame (should be "odom" in most cases)
scan_topic: the name of the scan topic to listen to (should be "scan" in most cases)
n_particles: the number of particles in the filter
d_thresh: the amount of linear movement before triggering a filter update
a_thresh: the amount of angular movement before triggering a filter update
laser_max_distance: the maximum distance to an obstacle we should use in a likelihood calculation
pose_listener: a subscriber that listens for new approximate pose estimates (i.e. generated through the rviz GUI)
particle_pub: a publisher for the particle cloud
laser_subscriber: listens for new scan data on topic self.scan_topic
tf_listener: listener for coordinate transforms
tf_broadcaster: broadcaster for coordinate transforms
particle_cloud: a list of particles representing a probability distribution over robot poses
current_odom_xy_theta: the pose of the robot in the odometry frame when the last filter update was performed.
The pose is expressed as a list [x,y,theta] (where theta is the yaw)
map: the map we will be localizing ourselves in. The map should be of type nav_msgs/OccupancyGrid
"""
def __init__(self):
self.initialized = False # make sure we don't perform updates before everything is setup
rospy.init_node(
'pf') # tell roscore that we are creating a new node named "pf"
self.base_frame = "base_link" # the frame of the robot base
self.map_frame = "map" # the name of the map coordinate frame
self.odom_frame = "odom" # the name of the odometry coordinate frame
self.scan_topic = "scan" # the topic where we will get laser scans from
self.n_particles = 500 # the number of particles to use
self.d_thresh = 0.1 # the amount of linear movement before performing an update
self.a_thresh = math.pi / 12 # the amount of angular movement before performing an update
self.laser_max_distance = 2.0 # maximum penalty to assess in the likelihood field model
# Setup pubs and subs
# pose_listener responds to selection of a new approximate robot location (for instance using rviz)
rospy.Subscriber("initialpose", PoseWithCovarianceStamped,
self.update_initial_pose)
# publish the current particle cloud. This enables viewing particles in rviz.
self.particle_pub = rospy.Publisher("particlecloud",
PoseArray,
queue_size=10)
# laser_subscriber listens for data from the lidar
rospy.Subscriber(self.scan_topic, LaserScan, self.scan_received)
# enable listening for and broadcasting coordinate transforms
self.tf_listener = TransformListener()
self.tf_broadcaster = TransformBroadcaster()
self.particle_cloud = []
# change use_projected_stable_scan to True to use point clouds instead of laser scans
self.use_projected_stable_scan = False
self.last_projected_stable_scan = None
if self.use_projected_stable_scan:
# subscriber to the odom point cloud
rospy.Subscriber("projected_stable_scan", PointCloud,
self.projected_scan_received)
self.current_odom_xy_theta = []
# request the map from the map server
rospy.wait_for_service('static_map')
try:
map_server = rospy.ServiceProxy('static_map', GetMap)
map = map_server().map
print map.info.resolution
except:
print "Service call failed!"
# initializes the occupancyfield which contains the map
self.occupancy_field = OccupancyField(map)
print "initialized"
self.initialized = True
def update_robot_pose(self):
""" Update the estimate of the robot's pose given the updated particles.
There are two logical methods for this:
(1): compute the mean pose
(2): compute the most likely pose (i.e. the mode of the distribution)
"""
# first make sure that the particle weights are normalized
self.normalize_particles()
# for the pose, calculate the particle's mean location
mean_particle = Particle(0, 0, 0, 0)
mean_particle_theta_x = 0
mean_particle_theta_y = 0
for particle in self.particle_cloud:
mean_particle.x += particle.x * particle.w
mean_particle.y += particle.y * particle.w
# angle is calculated using trig to account for angle runover
distance_vector = np.sqrt(
np.square(particle.y) + np.square(particle.x))
mean_particle_theta_x += distance_vector * np.cos(
particle.theta) * particle.w
mean_particle_theta_y += distance_vector * np.sin(
particle.theta) * particle.w
mean_particle.theta = np.arctan2(float(mean_particle_theta_y),
float(mean_particle_theta_x))
self.robot_pose = mean_particle.as_pose()
def projected_scan_received(self, msg):
self.last_projected_stable_scan = msg
def update_particles_with_odom(self, msg):
""" Update the particles using the newly given odometry pose.
The function computes the value delta which is a tuple (x,y,theta)
that indicates the change in position and angle between the odometry
when the particles were last updated and the current odometry.
msg: this is not really needed to implement this, but is here just in case.
"""
new_odom_xy_theta = convert_pose_to_xy_and_theta(self.odom_pose.pose)
# compute the change in x,y,theta since our last update
if self.current_odom_xy_theta:
old_odom_xy_theta = self.current_odom_xy_theta
delta = (new_odom_xy_theta[0] - self.current_odom_xy_theta[0],
new_odom_xy_theta[1] - self.current_odom_xy_theta[1],
new_odom_xy_theta[2] - self.current_odom_xy_theta[2])
self.current_odom_xy_theta = new_odom_xy_theta
else:
self.current_odom_xy_theta = new_odom_xy_theta
return
odom_noise = .3 # level of noise put into particles after update from odom to introduce variability
# updates the particles based on r1, d, and r2. For more information on this, consult the website
for particle in self.particle_cloud:
# calculates r1, d, and r2
r1 = np.arctan2(float(delta[1]), float(
delta[0])) - old_odom_xy_theta[2]
d = np.sqrt(np.square(delta[0]) + np.square(delta[1]))
r2 = delta[2] - r1
# updates the particles with the above variables, while also adding in some noise
particle.theta = particle.theta + r1 * (
random_sample() * odom_noise + (1 - odom_noise / 2.0))
particle.x = particle.x + d * np.cos(
particle.theta) * (random_sample() * odom_noise +
(1 - odom_noise / 2.0))
particle.y = particle.y + d * np.sin(
particle.theta) * (random_sample() * odom_noise +
(1 - odom_noise / 2.0))
particle.theta = particle.theta + r2 * (
random_sample() * odom_noise + (1 - odom_noise / 2.0))
def resample_particles(self):
""" Resample the particles according to the new particle weights.
The weights stored with each particle should define the probability that a particular
particle is selected in the resampling step. You may want to make use of the given helper
function draw_random_sample.
"""
# make sure the distribution is normalized
self.normalize_particles()
# creates choices and probabilities lists, which are the particles and their respective weights
choices = []
probabilities = []
num_samples = len(self.particle_cloud)
for particle in self.particle_cloud:
choices.append(particle)
probabilities.append(particle.w)
# re-makes the particle cloud according to a random sample based on the probability distribution of the weights
self.particle_cloud = self.draw_random_sample(choices, probabilities,
num_samples)
def update_particles_with_laser(self, msg):
""" Updates the particle weights in response to the scan contained in the msg """
# for each particle, find the total error based on 36 laser measurements taken from the Neato's actual position
for particle in self.particle_cloud:
error = []
for theta in range(0, 360, 10):
rad = np.radians(theta)
err = self.occupancy_field.get_closest_obstacle_distance(
particle.x +
msg.ranges[theta] * np.cos(particle.theta + rad),
particle.y +
msg.ranges[theta] * np.sin(particle.theta + rad))
if (
math.isnan(err)
): # if the get_closest_obstacle_distance method finds that a point is out of bounds, then the particle can't never be it
particle.w = 0
break
error.append(
err**5
) # each error is appended up to a power to make more likely particles have higher probability
if (
sum(error) == 0
): # if the particle is basically a perfect match, then we make the particle almost always enter the next iteration through resampling
particle.w = 1.0
else:
particle.w = 1.0 / sum(
error
) # the errors are inverted such that large errors become small and small errors become large
@staticmethod
def draw_random_sample(choices, probabilities, n):
""" Return a random sample of n elements from the set choices with the specified probabilities
choices: the values to sample from represented as a list
probabilities: the probability of selecting each element in choices represented as a list
n: the number of samples
"""
# sets up an index list for the chosen particles, and makes bins for the probabilities
values = np.array(range(len(choices)))
probs = np.array(probabilities)
bins = np.add.accumulate(probs)
inds = values[np.digitize(
random_sample(n), bins
)] # chooses the new particles based on the probabilities of the old ones
samples = []
for i in inds:
samples.append(
deepcopy(choices[int(i)])
) # makes the new particle cloud based on the chosen particles
return samples
def update_initial_pose(self, msg):
""" Callback function to handle re-initializing the particle filter based on a pose estimate.
These pose estimates could be generated by another ROS Node or could come from the rviz GUI """
xy_theta = convert_pose_to_xy_and_theta(msg.pose.pose)
self.initialize_particle_cloud(xy_theta)
self.fix_map_to_odom_transform(msg)
def initialize_particle_cloud(self, xy_theta=None):
""" Initialize the particle cloud.
Arguments
xy_theta: a triple consisting of the mean x, y, and theta (yaw) to initialize the
particle cloud around. If this input is ommitted, the odometry will be used """
# levels of noise to introduce variability
lin_noise = 1
ang_noise = math.pi / 2.0
# if doesn't exist, use odom
if xy_theta == None:
xy_theta = convert_pose_to_xy_and_theta(self.odom_pose.pose)
# make a new particle cloud, and then create a bunch of particles at the initial location with some added noise
self.particle_cloud = []
for x in range(self.n_particles):
x = xy_theta[0] + (random_sample() * lin_noise - (lin_noise / 2.0))
y = xy_theta[1] + (random_sample() * lin_noise - (lin_noise / 2.0))
theta = xy_theta[2] + (random_sample() * ang_noise -
(ang_noise / 2.0))
self.particle_cloud.append(Particle(x, y, theta))
# normalize particles because all weights were originall set to 1 on default
self.normalize_particles()
self.update_robot_pose()
def normalize_particles(self):
""" Make sure the particle weights define a valid distribution (i.e. sum to 1.0) """
# takes the sum, and then divides all weights by the sum
weights_sum = sum(particle.w for particle in self.particle_cloud)
for particle in self.particle_cloud:
particle.w /= weights_sum
def publish_particles(self, msg):
"""Publishes the particles out for visualization and other purposes"""
particles_conv = []
for p in self.particle_cloud:
particles_conv.append(p.as_pose())
# actually send the message so that we can view it in rviz
self.particle_pub.publish(
PoseArray(header=Header(stamp=rospy.Time.now(),
frame_id=self.map_frame),
poses=particles_conv))
def scan_received(self, msg):
""" This is the default logic for what to do when processing scan data.
Feel free to modify this, however, I hope it will provide a good
guide. The input msg is an object of type sensor_msgs/LaserScan """
if not (self.initialized):
# wait for initialization to complete
return
if not (self.tf_listener.canTransform(
self.base_frame, msg.header.frame_id, msg.header.stamp)):
# need to know how to transform the laser to the base frame
# this will be given by either Gazebo or neato_node
return
if not (self.tf_listener.canTransform(self.base_frame, self.odom_frame,
msg.header.stamp)):
# need to know how to transform between base and odometric frames
# this will eventually be published by either Gazebo or neato_node
return
# calculate pose of laser relative ot the robot base
p = PoseStamped(
header=Header(stamp=rospy.Time(0), frame_id=msg.header.frame_id))
self.laser_pose = self.tf_listener.transformPose(self.base_frame, p)
# find out where the robot thinks it is based on its odometry
p = PoseStamped(header=Header(stamp=msg.header.stamp,
frame_id=self.base_frame),
pose=Pose())
self.odom_pose = self.tf_listener.transformPose(self.odom_frame, p)
# store the the odometry pose in a more convenient format (x,y,theta)
new_odom_xy_theta = convert_pose_to_xy_and_theta(self.odom_pose.pose)
if not (self.particle_cloud):
# now that we have all of the necessary transforms we can update the particle cloud
self.initialize_particle_cloud()
# cache the last odometric pose so we can only update our particle filter if we move more than self.d_thresh or self.a_thresh
self.current_odom_xy_theta = new_odom_xy_theta
# update our map to odom transform now that the particles are initialized
self.fix_map_to_odom_transform(msg)
elif (math.fabs(new_odom_xy_theta[0] - self.current_odom_xy_theta[0]) >
self.d_thresh
or math.fabs(new_odom_xy_theta[1] -
self.current_odom_xy_theta[1]) > self.d_thresh
or math.fabs(new_odom_xy_theta[2] -
self.current_odom_xy_theta[2]) > self.a_thresh):
# we have moved far enough to do an update!
self.update_particles_with_odom(msg) # update based on odometry
if self.last_projected_stable_scan:
last_projected_scan_timeshift = deepcopy(
self.last_projected_stable_scan)
last_projected_scan_timeshift.header.stamp = msg.header.stamp
self.scan_in_base_link = self.tf_listener.transformPointCloud(
"base_link", last_projected_scan_timeshift)
self.update_particles_with_laser(msg) # update based on laser scan
self.update_robot_pose() # update robot's pose
self.resample_particles(
) # resample particles to focus on areas of high density
self.fix_map_to_odom_transform(
msg
) # update map to odom transform now that we have new particles
# publish particles (so things like rviz can see them)
self.publish_particles(msg)
def fix_map_to_odom_transform(self, msg):
""" This method constantly updates the offset of the map and
odometry coordinate systems based on the latest results from
the localizer"""
(translation,
rotation) = convert_pose_inverse_transform(self.robot_pose)
p = PoseStamped(pose=convert_translation_rotation_to_pose(
translation, rotation),
header=Header(stamp=msg.header.stamp,
frame_id=self.base_frame))
self.tf_listener.waitForTransform(self.base_frame,
self.odom_frame, msg.header.stamp,
rospy.Duration(1.0))
self.odom_to_map = self.tf_listener.transformPose(self.odom_frame, p)
(self.translation,
self.rotation) = convert_pose_inverse_transform(self.odom_to_map.pose)
def broadcast_last_transform(self):
""" Make sure that we are always broadcasting the last map
to odom transformation. This is necessary so things like
move_base can work properly. """
if not (hasattr(self, 'translation') and hasattr(self, 'rotation')):
return
self.tf_broadcaster.sendTransform(self.translation, self.rotation,
rospy.get_rostime(), self.odom_frame,
self.map_frame)
class ParticleFilter:
""" The class that represents a Particle Filter ROS Node
Attributes list:
initialized: a Boolean flag to communicate to other class methods that initializaiton is complete
base_frame: the name of the robot base coordinate frame (should be "base_link" for most robots)
map_frame: the name of the map coordinate frame (should be "map" in most cases)
odom_frame: the name of the odometry coordinate frame (should be "odom" in most cases)
scan_topic: the name of the scan topic to listen to (should be "scan" in most cases)
n_particles: the number of particles in the filter
d_thresh: the amount of linear movement before triggering a filter update
a_thresh: the amount of angular movement before triggering a filter update
laser_max_distance: the maximum distance to an obstacle we should use in a likelihood calculation
pose_listener: a subscriber that listens for new approximate pose estimates (i.e. generated through the rviz GUI)
particle_pub: a publisher for the particle cloud
laser_subscriber: listens for new scan data on topic self.scan_topic
tf_listener: listener for coordinate transforms
tf_broadcaster: broadcaster for coordinate transforms
particle_cloud: a list of particles representing a probability distribution over robot poses
current_odom_xy_theta: the pose of the robot in the odometry frame when the last filter update was performed.
The pose is expressed as a list [x,y,theta] (where theta is the yaw)
map: the map we will be localizing ourselves in. The map should be of type nav_msgs/OccupancyGrid
"""
def __init__(self):
self.initialized = False # make sure we don't perform updates before everything is setup
rospy.init_node('pf') # tell roscore that we are creating a new node named "pf"
self.base_frame = "base_link" # the frame of the robot base
self.map_frame = "map" # the name of the map coordinate frame
self.odom_frame = "odom" # the name of the odometry coordinate frame
self.scan_topic = "scan" # the topic where we will get laser scans from
self.n_particles = 300 # the number of particles to use
self.model_noise_rate = 0.15
self.d_thresh = .2 # the amount of linear movement before performing an update
self.a_thresh = math.pi / 6 # the amount of angular movement before performing an update
self.laser_max_distance = 2.0 # maximum penalty to assess in the likelihood field model
# TODO: define additional constants if needed
# Setup pubs and subs
# pose_listener responds to selection of a new approximate robot location (for instance using rviz)
self.pose_listener = rospy.Subscriber("initialpose", PoseWithCovarianceStamped, self.update_initial_pose)
# publish the current particle cloud. This enables viewing particles in rviz.
self.particle_pub = rospy.Publisher("particlecloud", PoseArray, queue_size=10)
# ?????
# rospy.Subscriber('/simple_odom', Odometry, self.process_odom)
# laser_subscriber listens for data from the lidar
self.laser_subscriber = rospy.Subscriber(self.scan_topic, LaserScan, self.scan_received, queue_size=10)
# enable listening for and broadcasting coordinate transforms
self.tf_listener = TransformListener()
self.tf_broadcaster = TransformBroadcaster()
self.particle_cloud = []
self.current_odom_xy_theta = [] # [.0] * 3
# self.initial_particles = self.initial_list_builder()
# self.particle_cloud = self.initialize_particle_cloud()
print(self.particle_cloud)
# self.current_odom_xy_theta = convert_pose_to_xy_and_theta(self.odom_pose.pose)
# request the map from the map server, the map should be of type nav_msgs/OccupancyGrid
# TODO: fill in the appropriate service call here. The resultant map should be assigned be passed
# into the init method for OccupancyField
# for now we have commented out the occupancy field initialization until you can successfully fetch the map
mapa = obter_mapa()
self.occupancy_field = OccupancyField(mapa)
# self.update_particles_with_odom(msg)
self.initialized = True
def update_robot_pose(self):
""" Update the estimate of the robot's pose given the updated particles.
There are two logical methods for this:
(1): compute the mean pose
(2): compute the most likely pose (i.e. the mode of the distribution)
"""
# first make sure that the particle weights are normalized
self.normalize_particles()
# TODO: assign the lastest pose into self.robot_pose as a geometry_msgs.Pose object
# just to get started we will fix the robot's pose to always be at the origin
self.robot_pose = Pose()
def update_particles_with_odom(self, msg):
""" Update the particles using the newly given odometry pose.
The function computes the value delta which is a tuple (x,y,theta)
that indicates the change in position and angle between the odometry
when the particles were last updated and the current odometry.
msg: this is not really needed to implement this, but is here just in case.
"""
new_odom_xy_theta = convert_pose_to_xy_and_theta(self.odom_pose.pose)
print(new_odom_xy_theta)
# compute the change in x,y,theta since our last update
if self.current_odom_xy_theta:
new_odom_xy_theta = convert_pose_to_xy_and_theta(self.odom_pose.pose)
old_odom_xy_theta = self.current_odom_xy_theta
delta = (new_odom_xy_theta[0] - self.current_odom_xy_theta[0],
new_odom_xy_theta[1] - self.current_odom_xy_theta[1],
new_odom_xy_theta[2] - self.current_odom_xy_theta[2])
for p in self.particle_cloud:
p.x += delta[0]
p.y += delta[1]
p.theta += delta[2]
self.current_odom_xy_theta = new_odom_xy_theta
else:
self.current_odom_xy_theta = new_odom_xy_theta
return # ????
# TODO: modify particles using delta
for p in self.particle_cloud:
r = math.atan2(delta[1], delta[0]) - old_odom_xy_theta[2]
d = math.sqrt((delta[0] ** 2) + (delta[1] ** 2))
p.theta += r % 360
p.x += d * math.cos(p.theta) + normal(0, .1)
p.y += d * math.sin(p.theta) + normal(0, .1)
p.theta += (delta[2] - r + normal(0, .1)) % 360
# For added difficulty: Implement sample_motion_odometry (Prob Rob p 136)
def map_calc_range(self,x,y,theta):
""" Difficulty Level 3: implement a ray tracing likelihood model... Let me know if you are interested """
# TODO: nothing unless you want to try this alternate likelihood model
pass
def resample_particles(self):
""" Resample the particles according to the new particle weights.
The weights stored with each particle should define the probability that a particular
particle is selected in the resampling step. You may want to make use of the given helper
function draw_random_sample.
"""
# make sure the distribution is normalized
# TODO: fill out the rest of the implementation
self.particle_cloud = ParticleFilter.weighted_values(self.particle_cloud,
[p.w for p in self.particle_cloud],
len(self.particle_cloud))
for p in particle_cloud:
p.w = 1 / len(self.particle_cloud)
self.normalize_particles()
def update_particles_with_laser(self, msg):
""" Updates the particle weights in response to the scan contained in the msg """
# TODO: implement this
for r in msg.ranges:
for p in self.particle_cloud:
p.w = 1
self.occupancy_field.get_particle_likelyhood(p, r, self.model_noise_rate)
self.normalize_particles()
self.resample_particles()
@staticmethod
def weighted_values(values, probabilities, size):
""" Return a random sample of size elements from the set values with the specified probabilities
values: the values to sample from (numpy.ndarray)
probabilities: the probability of selecting each element in values (numpy.ndarray)
size: the number of samples
"""
bins = np.add.accumulate(probabilities)
print(size, bins)
return values[np.digitize(random_sample(size), bins)]
@staticmethod
def draw_random_sample(choices, probabilities, n):
""" Return a random sample of n elements from the set choices with the specified probabilities
choices: the values to sample from represented as a list
probabilities: the probability of selecting each element in choices represented as a list
n: the number of samples
"""
values = np.array(range(len(choices)))
probs = np.array(probabilities)
bins = np.add.accumulate(probs)
inds = values[np.digitize(random_sample(n), bins)]
samples = []
for i in inds:
samples.append(deepcopy(choices[int(i)]))
return samples
def update_initial_pose(self, msg):
""" Callback function to handle re-initializing the particle filter based on a pose estimate.
These pose estimates could be generated by another ROS Node or could come from the rviz GUI """
xy_theta = convert_pose_to_xy_and_theta(msg.pose.pose)
self.initialize_particle_cloud(xy_theta)
self.fix_map_to_odom_transform(msg)
def initialize_particle_cloud(self, xy_theta=None):
""" Initialize the particle cloud.
Arguments
xy_theta: a triple consisting of the mean x, y, and theta (yaw) to initialize the
particle cloud around. If this input is ommitted, the odometry will be used """
if xy_theta == None:
xy_theta = convert_pose_to_xy_and_theta(self.odom_pose.pose)
# TODO create particles
self.particle_cloud = self.initial_list_builder(xy_theta)
self.normalize_particles()
self.update_robot_pose()
def normalize_particles(self):
""" Make sure the particle weights define a valid distribution (i.e. sum to 1.0) """
# TODO: implement this
w_sum = 0
for p in self.particle_cloud:
w_sum += p.w
for p in self.particle_cloud:
p.w /= w_sum
def publish_particles(self, msg):
particles_conv = []
for p in self.particle_cloud:
particles_conv.append(p.as_pose())
# actually send the message so that we can view it in rviz
self.particle_pub.publish(PoseArray(header=Header(stamp=rospy.Time.now(),
frame_id=self.map_frame),
poses=particles_conv))
def scan_received(self, msg):
""" This is the default logic for what to do when processing scan data.
Feel free to modify this, however, I hope it will provide a good
guide. The input msg is an object of type sensor_msgs/LaserScan """
if not(self.initialized):
# wait for initialization to complete
# print 1
return
if not(self.tf_listener.canTransform(self.base_frame,msg.header.frame_id,rospy.Time(0))):
# need to know how to transform the laser to the base frame
# this will be given by either Gazebo or neato_node
print 2
return
if not(self.tf_listener.canTransform(self.base_frame,self.odom_frame,rospy.Time(0))):
# need to know how to transform between base and odometric frames
# this will eventually be published by either Gazebo or neato_node
print 3
return
# calculate pose of laser relative ot the robot base
p = PoseStamped(header=Header(stamp=rospy.Time(0),
frame_id=msg.header.frame_id))
self.laser_pose = self.tf_listener.transformPose(self.base_frame,p)
# find out where the robot thinks it is based on its odometry
p = PoseStamped(header=Header(stamp=rospy.Time(0),
frame_id=self.base_frame),
pose=Pose())
self.odom_pose = self.tf_listener.transformPose(self.odom_frame, p)
# store the the odometry pose in a more convenient format (x,y,theta)
new_odom_xy_theta = convert_pose_to_xy_and_theta(self.odom_pose.pose)
print(self.current_odom_xy_theta) # Essa list não está sendo "refeita" / preenchida
print(new_odom_xy_theta)
if not(self.particle_cloud):
# now that we have all of the necessary transforms we can update the particle cloud
self.initialize_particle_cloud()
# cache the last odometric pose so we can only update our particle filter if we move more than self.d_thresh or self.a_thresh
self.current_odom_xy_theta = new_odom_xy_theta
# update our map to odom transform now that the particles are initialized
self.fix_map_to_odom_transform(msg)
print(math.fabs(new_odom_xy_theta[0] - self.current_odom_xy_theta[0]), "hi")
elif (math.fabs(new_odom_xy_theta[0] - self.current_odom_xy_theta[0]) > self.d_thresh or
math.fabs(new_odom_xy_theta[1] - self.current_odom_xy_theta[1]) > self.d_thresh or
math.fabs(new_odom_xy_theta[2] - self.current_odom_xy_theta[2]) > self.a_thresh):
# we have moved far enough to do an update!
'''
É AQUI!!!!
'''
print('jorge')
self.update_particles_with_odom(msg) # update based on odometry - FAZER
self.update_particles_with_laser(msg) # update based on laser scan - FAZER
self.update_robot_pose() # update robot's pose
""" abaixo """
self.resample_particles() # resample particles to focus on areas of high density - FAZER
self.fix_map_to_odom_transform(msg) # update map to odom transform now that we have new particles
# publish particles (so things like rviz can see them)
self.publish_particles(msg)
def fix_map_to_odom_transform(self, msg):
""" This method constantly updates the offset of the map and
odometry coordinate systems based on the latest results from
the localizer """
(translation, rotation) = convert_pose_inverse_transform(self.robot_pose)
p = PoseStamped(pose=convert_translation_rotation_to_pose(translation,rotation),
header=Header(stamp=rospy.Time(0),frame_id=self.base_frame))
self.odom_to_map = self.tf_listener.transformPose(self.odom_frame, p)
(self.translation, self.rotation) = convert_pose_inverse_transform(self.odom_to_map.pose)
def broadcast_last_transform(self):
""" Make sure that we are always broadcasting the last map
to odom transformation. This is necessary so things like
move_base can work properly. """
if not(hasattr(self,'translation') and hasattr(self,'rotation')):
return
self.tf_broadcaster.sendTransform(self.translation,
self.rotation,
rospy.Time.now(),
self.odom_frame,
self.map_frame)
def initial_list_builder(self, xy_theta):
'''
Creates the initial particles list,
using the super advanced methods
provided to us by the one and only
John
Cena
'''
initial_particles = []
for i in range(self.n_particles):
p = Particle()
p.x = gauss(xy_theta[0], 1)
p.y = gauss(xy_theta[1], 1)
p.theta = gauss(xy_theta[2], (math.pi / 2))
p.w = 1.0 / self.n_particles
initial_particles.append(p)
return initial_particles
class ParticleFilter:
""" The class that represents a Particle Filter ROS Node
Attributes list:
initialized: a Boolean flag to communicate to other class methods that initializaiton is complete
base_frame: the name of the robot base coordinate frame (should be "base_link" for most robots)
map_frame: the name of the map coordinate frame (should be "map" in most cases)
odom_frame: the name of the odometry coordinate frame (should be "odom" in most cases)
scan_topic: the name of the scan topic to listen to (should be "scan" in most cases)
n_particles: the number of particles in the filter
d_thresh: the amount of linear movement before triggering a filter update
a_thresh: the amount of angular movement before triggering a filter update
laser_max_distance: the maximum distance to an obstacle we should use in a likelihood calculation
pose_listener: a subscriber that listens for new approximate pose estimates (i.e. generated through the rviz GUI)
particle_pub: a publisher for the particle cloud
laser_subscriber: listens for new scan data on topic self.scan_topic
tf_listener: listener for coordinate transforms
tf_broadcaster: broadcaster for coordinate transforms
particle_cloud: a list of particles representing a probability distribution over robot poses
current_odom_xy_theta: the pose of the robot in the odometry frame when the last filter update was performed.
The pose is expressed as a list [x,y,theta] (where theta is the yaw)
map: the map we will be localizing ourselves in. The map should be of type nav_msgs/OccupancyGrid
"""
def __init__(self):
self.initialized = False # make sure we don't perform updates before everything is setup
rospy.init_node('pf') # tell roscore that we are creating a new node named "pf"
self.base_frame = "base_link" # the frame of the robot base
self.map_frame = "map" # the name of the map coordinate frame
self.odom_frame = "odom" # the name of the odometry coordinate frame
self.scan_topic = "scan" # the topic where we will get laser scans from
self.n_particles = 300 # the number of particles to use
self.d_thresh = 0.2 # the amount of linear movement before performing an update
self.a_thresh = math.pi/6 # the amount of angular movement before performing an update
self.laser_max_distance = 2.0 # maximum penalty to assess in the likelihood field model
self.robot_pose
# TODO: define additional constants if needed
# Setup pubs and subs
# pose_listener responds to selection of a new approximate robot location (for instance using rviz)
self.pose_listener = rospy.Subscriber("initialpose", PoseWithCovarianceStamped, self.update_initial_pose)
# publish the current particle cloud. This enables viewing particles in rviz.
self.particle_pub = rospy.Publisher("particlecloud", PoseArray)
# laser_subscriber listens for data from the lidar
self.laser_subscriber = rospy.Subscriber(self.scan_topic, LaserScan, self.scan_received)
# enable listening for and broadcasting coordinate transforms
self.tf_listener = TransformListener()
self.tf_broadcaster = TransformBroadcaster()
self.particle_cloud = []
self.current_odom_xy_theta = []
# request the map from the map server, the map should be of type nav_msgs/OccupancyGrid
# TODO: fill in the appropriate service call here. The resultant map should be assigned be passed
# into the init method for OccupancyField
self.occupancy_field = OccupancyField(map)
self.initialized = True
def update_robot_pose(self):
""" Update the estimate of the robot's pose given the updated particles.
There are two logical methods for this:
(1): compute the mean pose (level 2)
(2): compute the most likely pose (i.e. the mode of the distribution) (level 1)
"""
# TODO: assign the lastest pose into self.robot_pose as a geometry_msgs.Pose object
# first make sure that the particle weights are normalized
self.normalize_particles()
def update_particles_with_odom(self, msg):
""" Implement a simple version of this (Level 1) or a more complex one (Level 2) """
new_odom_xy_theta = TransformHelpers.convert_pose_to_xy_and_theta(self.odom_pose.pose)
# compute the change in x,y,theta since our last update
if self.current_odom_xy_theta:
old_odom_xy_theta = self.current_odom_xy_theta
delta = (new_odom_xy_theta[0] - self.current_odom_xy_theta[0], new_odom_xy_theta[1] - self.current_odom_xy_theta[1], new_odom_xy_theta[2] - self.current_odom_xy_theta[2])
self.current_odom_xy_theta = new_odom_xy_theta
else:
self.current_odom_xy_theta = new_odom_xy_theta
return
# TODO: modify particles using delta
# For added difficulty: Implement sample_motion_odometry (Prob Rob p 136)
def map_calc_range(self,x,y,theta):
""" Difficulty Level 3: implement a ray tracing likelihood model... Let me know if you are interested """
# TODO: nothing unless you want to try this alternate likelihood model
pass
def resample_particles(self):
""" Resample the particles according to the new particle weights """
# make sure the distribution is normalized
self.normalize_particles()
# TODO: fill out the rest of the implementation
def update_particles_with_laser(self, msg):
""" Updates the particle weights in response to the scan contained in the msg """
# TODO: implement this
pass
@staticmethod
def angle_normalize(z):
""" convenience function to map an angle to the range [-pi,pi] """
return math.atan2(math.sin(z), math.cos(z))
@staticmethod
def angle_diff(a, b):
""" Calculates the difference between angle a and angle b (both should be in radians)
the difference is always based on the closest rotation from angle a to angle b
examples:
angle_diff(.1,.2) -> -.1
angle_diff(.1, 2*math.pi - .1) -> .2
angle_diff(.1, .2+2*math.pi) -> -.1
"""
a = ParticleFilter.angle_normalize(a)
b = ParticleFilter.angle_normalize(b)
d1 = a-b
d2 = 2*math.pi - math.fabs(d1)
if d1 > 0:
d2 *= -1.0
if math.fabs(d1) < math.fabs(d2):
return d1
else:
return d2
@staticmethod
def weighted_values(values, probabilities, size):
""" Return a random sample of size elements form the set values with the specified probabilities
values: the values to sample from (numpy.ndarray)
probabilities: the probability of selecting each element in values (numpy.ndarray)
size: the number of samples
"""
bins = np.add.accumulate(probabilities)
return values[np.digitize(random_sample(size), bins)]
def update_initial_pose(self, msg):
""" Callback function to handle re-initializing the particle filter based on a pose estimate.
These pose estimates could be generated by another ROS Node or could come from the rviz GUI """
xy_theta = TransformHelpers.convert_pose_to_xy_and_theta(msg.pose.pose)
self.initialize_particle_cloud(xy_theta)
self.fix_map_to_odom_transform(msg)
def initialize_particle_cloud(self, xy_theta=None):
""" Initialize the particle cloud.
Arguments
xy_theta: a triple consisting of the mean x, y, and theta (yaw) to initialize the
particle cloud around. If this input is ommitted, the odometry will be used """
if xy_theta == None:
xy_theta = TransformHelpers.convert_pose_to_xy_and_theta(self.odom_pose.pose)
self.particle_cloud = []
# TODO create particles
self.normalize_particles()
self.update_robot_pose()
def normalize_particles(self):
""" Make sure the particle weights define a valid distribution (i.e. sum to 1.0) """
# TODO: implement this
def publish_particles(self, msg):
particles_conv = []
for p in self.particle_cloud:
particles_conv.append(p.as_pose())
# actually send the message so that we can view it in rviz
self.particle_pub.publish(PoseArray(header=Header(stamp=rospy.Time.now(),frame_id=self.map_frame),poses=particles_conv))
def scan_received(self, msg):
""" This is the default logic for what to do when processing scan data. Feel free to modify this, however,
I hope it will provide a good guide. The input msg is an object of type sensor_msgs/LaserScan """
if not(self.initialized):
# wait for initialization to complete
return
if not(self.tf_listener.canTransform(self.base_frame,msg.header.frame_id,msg.header.stamp)):
# need to know how to transform the laser to the base frame
# this will be given by either Gazebo or neato_node
return
if not(self.tf_listener.canTransform(self.base_frame,self.odom_frame,msg.header.stamp)):
# need to know how to transform between base and odometric frames
# this will eventually be published by either Gazebo or neato_node
return
# calculate pose of laser relative ot the robot base
p = PoseStamped(header=Header(stamp=rospy.Time(0),frame_id=msg.header.frame_id))
self.laser_pose = self.tf_listener.transformPose(self.base_frame,p)
# find out where the robot thinks it is based on its odometry
p = PoseStamped(header=Header(stamp=msg.header.stamp,frame_id=self.base_frame), pose=Pose())
self.odom_pose = self.tf_listener.transformPose(self.odom_frame, p)
# store the the odometry pose in a more convenient format (x,y,theta)
new_odom_xy_theta = TransformHelpers.convert_pose_to_xy_and_theta(self.odom_pose.pose)
if not(self.particle_cloud):
# now that we have all of the necessary transforms we can update the particle cloud
self.initialize_particle_cloud()
# cache the last odometric pose so we can only update our particle filter if we move more than self.d_thresh or self.a_thresh
self.current_odom_xy_theta = new_odom_xy_theta
# update our map to odom transform now that the particles are initialized
self.fix_map_to_odom_transform(msg)
elif (math.fabs(new_odom_xy_theta[0] - self.current_odom_xy_theta[0]) > self.d_thresh or
math.fabs(new_odom_xy_theta[1] - self.current_odom_xy_theta[1]) > self.d_thresh or
math.fabs(new_odom_xy_theta[2] - self.current_odom_xy_theta[2]) > self.a_thresh):
# we have moved far enough to do an update!
self.update_particles_with_odom(msg) # update based on odometry
self.update_particles_with_laser(msg) # update based on laser scan
self.resample_particles() # resample particles to focus on areas of high density
self.update_robot_pose() # update robot's pose
self.fix_map_to_odom_transform(msg) # update map to odom transform now that we have new particles
# publish particles (so things like rviz can see them)
self.publish_particles(msg)
def fix_map_to_odom_transform(self, msg):
""" Super tricky code to properly update map to odom transform... do not modify this... Difficulty level infinity. """
(translation, rotation) = TransformHelpers.convert_pose_inverse_transform(self.robot_pose)
p = PoseStamped(pose=TransformHelpers.convert_translation_rotation_to_pose(translation,rotation),header=Header(stamp=msg.header.stamp,frame_id=self.base_frame))
self.odom_to_map = self.tf_listener.transformPose(self.odom_frame, p)
(self.translation, self.rotation) = TransformHelpers.convert_pose_inverse_transform(self.odom_to_map.pose)
def broadcast_last_transform(self):
""" Make sure that we are always broadcasting the last map to odom transformation.
This is necessary so things like move_base can work properly. """
if not(hasattr(self,'translation') and hasattr(self,'rotation')):
return
self.tf_broadcaster.sendTransform(self.translation, self.rotation, rospy.get_rostime(), self.odom_frame, self.map_frame)
class ParticleFilter:
""" The class that represents a Particle Filter ROS Node
Attributes list:
initialized: a Boolean flag to communicate to other class methods that initializaiton is complete
base_frame: the name of the robot base coordinate frame (should be "base_link" for most robots)
map_frame: the name of the map coordinate frame (should be "map" in most cases)
odom_frame: the name of the odometry coordinate frame (should be "odom" in most cases)
scan_topic: the name of the scan topic to listen to (should be "scan" in most cases)
start_particles: the number of particles first initalized
end_particles: the number of particles which end in the filter
middle_step: the step at which the number of particles has decayed about 50%
d_thresh: the amount of linear movement before triggering a filter update
a_thresh: the amount of angular movement before triggering a filter update
laser_max_distance: the maximum distance to an obstacle we should use in a likelihood calculation
pose_listener: a subscriber that listens for new approximate pose estimates (i.e. generated through the rviz GUI)
particle_pub: a publisher for the particle cloud
laser_subscriber: listens for new scan data on topic self.scan_topic
tf_listener: listener for coordinate transforms
tf_broadcaster: broadcaster for coordinate transforms
particle_cloud: a list of particles representing a probability distribution over robot poses
current_odom_xy_theta: the pose of the robot in the odometry frame when the last filter update was performed.
The pose is expressed as a list [x,y,theta] (where theta is the yaw)
map: the map we will be localizing ourselves in. The map should be of type nav_msgs/OccupancyGrid
"""
def __init__(self):
""" Define a new particle filter
"""
print("RUNNING")
self.initialized = False # make sure we don't perform updates before everything is setup
self.kidnap = False
rospy.init_node(
'pf') # tell roscore that we are creating a new node named "pf"
self.base_frame = "base_link" # the frame of the robot base
self.map_frame = "map" # the name of the map coordinate frame
self.odom_frame = "odom" # the name of the odometry coordinate frame
self.scan_topic = "scan" # the topic where we will get laser scans from
self.start_particles = 1000 # the number of particles to use
self.end_particles = 200
self.resample_count = 10
self.middle_step = 10
self.d_thresh = 0.2 # the amount of linear movement before performing an update
self.a_thresh = math.pi / 6 # the amount of angular movement before performing an update
self.laser_max_distance = 2.0 # maximum penalty to assess in the likelihood field model
# Setup pubs and subs
# pose_listener responds to selection of a new approximate robot location (for instance using rviz)
rospy.Subscriber("initialpose", PoseWithCovarianceStamped,
self.update_initial_pose)
# publish the current particle cloud. This enables viewing particles in rviz.
self.particle_pub = rospy.Publisher("particlecloud",
PoseArray,
queue_size=10)
# publish weights for live graph node
self.weight_pub = rospy.Publisher("/graph_data",
Float64MultiArray,
queue_size=10)
# laser_subscriber listens for data from the lidar
rospy.Subscriber(self.scan_topic, LaserScan, self.scan_received)
# enable listening for and broadcasting coordinate transforms
self.tf_listener = TransformListener()
self.tf_broadcaster = TransformBroadcaster()
self.particle_cloud = []
# change use_projected_stable_scan to True to use point clouds instead of laser scans
self.use_projected_stable_scan = False
self.last_projected_stable_scan = None
if self.use_projected_stable_scan:
# subscriber to the odom point cloud
rospy.Subscriber("projected_stable_scan", PointCloud,
self.projected_scan_received)
self.current_odom_xy_theta = []
self.occupancy_field = OccupancyField()
self.transform_helper = TFHelper()
# publish the marker array
# self.viz = rospy.Publisher('/particle_marker', Marker, queue_size=10)
# self.marker = Marker()
self.viz = rospy.Publisher('/particle_marker',
MarkerArray,
queue_size=10)
self.markerArray = MarkerArray()
self.initialized = True
# assigns robot pose. used only a visual debugger, the real data comes from the bag file.
def update_robot_pose(self, timestamp):
#print("Guessing Robot Position")
self.normalize_particles(self.particle_cloud)
weights = [p.w for p in self.particle_cloud]
index_best = weights.index(max(weights))
best_particle = self.particle_cloud[index_best]
self.robot_pose = self.transform_helper.covert_xy_and_theta_to_pose(
best_particle.x, best_particle.y, best_particle.theta)
self.transform_helper.fix_map_to_odom_transform(
self.robot_pose, timestamp)
def projected_scan_received(self, msg):
self.last_projected_stable_scan = msg
# deadreckons particles with respect to robot motion.
def update_particles_with_odom(self, msg):
""" To apply the robot transformations to a particle, it can be broken down into a rotations, a linear movement, and another rotation (which could equal 0)
"""
#print("Deadreckoning")
new_odom_xy_theta = self.transform_helper.convert_pose_to_xy_and_theta(
self.odom_pose.pose)
# compute the change in x,y,theta since our last update
if self.current_odom_xy_theta:
old_odom_xy_theta = self.current_odom_xy_theta
delta = (new_odom_xy_theta[0] - self.current_odom_xy_theta[0],
new_odom_xy_theta[1] - self.current_odom_xy_theta[1],
new_odom_xy_theta[2] - self.current_odom_xy_theta[2])
delta_x = delta[0]
delta_y = delta[1]
delta_theta = delta[2]
direction = math.atan2(delta_y, delta_x)
theta_1 = self.transform_helper.angle_diff(
direction, self.current_odom_xy_theta[2])
for p in self.particle_cloud:
distance = math.sqrt((delta_x**2) +
(delta_y**2)) + np.random.normal(
0, 0.001)
dx = distance * np.cos(p.theta + theta_1)
dy = distance * np.sin(p.theta + theta_1)
p.x += dx
p.y += dy
p.theta += delta_theta + np.random.normal(0, 0.005)
self.current_odom_xy_theta = new_odom_xy_theta
else:
self.current_odom_xy_theta = new_odom_xy_theta
return
def resample_particles(self):
""" Resample the particles according to the new particle weights.
The weights stored with each particle should define the probability that a particular
particle is selected in the resampling step. You may want to make use of the given helper
function draw_random_sample.
"""
# print("Resampling Particles")
# make sure the distribution is normalized
self.normalize_particles(self.particle_cloud)
particle_cloud_length = len(self.particle_cloud)
n_particles = ParticleFilter.sigmoid_function(self.resample_count,
self.start_particles,
self.end_particles,
self.middle_step, 1)
print("Number of Particles Reassigned: " + str(n_particles))
norm_weights = [p.w for p in self.particle_cloud]
# print("Weights: "+ str(norm_weights))
top_percent = 0.20
ordered_indexes = np.argsort(norm_weights)
ordered_particles = [
self.particle_cloud[index] for index in ordered_indexes
]
best_particles = ordered_particles[int(particle_cloud_length *
(1 - top_percent)):]
new_particles = ParticleFilter.draw_random_sample(
self.particle_cloud, norm_weights,
n_particles - int(particle_cloud_length * top_percent))
dist = 0.001 # adding a square meter of noise around each ideal particle
self.particle_cloud = []
self.particle_cloud += best_particles
for p in new_particles:
x_pos, y_pos, angle = p.x, p.y, p.theta
x_particle = np.random.normal(x_pos, dist)
y_particle = np.random.normal(y_pos, dist)
theta_particle = np.random.normal(angle, 0.05)
self.particle_cloud.append(
Particle(x_particle, y_particle, theta_particle))
self.normalize_particles(self.particle_cloud)
self.resample_count += 1
def update_particles_with_laser(self, msg):
""" Updates the particle weights in response to the scan contained in the msg """
#transform laser data from particle's perspective to map coords
#print("Assigning Weights")
scan = msg.ranges
num_particles = len(self.particle_cloud)
num_scans = 361
step = 2
angles = np.arange(num_scans) # will be scan indices (0-361)
distances = np.array(scan) # will be scan values (scan)
angles_rad = np.deg2rad(angles)
for p in self.particle_cloud:
sin_values = np.sin(angles_rad + p.theta)
cos_values = np.cos(angles_rad + p.theta)
d_angles_sin = np.multiply(distances, sin_values)
d_angles_cos = np.multiply(distances, cos_values)
d_angles_sin = d_angles_sin[0:361:step]
d_angles_cos = d_angles_cos[0:361:step]
total_beam_x = np.add(p.x, d_angles_cos)
total_beam_y = np.add(p.y, d_angles_sin)
particle_distances = self.occupancy_field.get_closest_obstacle_distance(
total_beam_x, total_beam_y)
cleaned_particle_distances = [
2 * np.exp(-(dist**2)) for dist in particle_distances
if (math.isnan(dist) != True)
]
p_d_cubed = np.power(cleaned_particle_distances, 3)
p.w = np.sum(p_d_cubed)
self.normalize_particles(self.particle_cloud)
@staticmethod
def draw_random_sample(choices, probabilities, n):
""" Return a random sample of n elements from the set choices with the specified probabilities
choices: the values to sample from represented as a list
probabilities: the probability of selecting each element in choices represented as a list
n: the number of samples
"""
values = np.array(range(len(choices)))
probs = np.array(probabilities)
bins = np.add.accumulate(probs)
inds = values[np.digitize(random_sample(n), bins)]
samples = []
for i in inds:
samples.append(deepcopy(choices[int(i)]))
return samples
def update_initial_pose(self, msg):
""" Callback function to handle re-initializing the particle filter based on a pose estimate.
These pose estimates could be generated by another ROS Node or could come from the rviz GUI """
#print("Initial Pose Set")
xy_theta = self.transform_helper.convert_pose_to_xy_and_theta(
msg.pose.pose)
self.initialize_particle_cloud(msg.header.stamp, xy_theta)
def initialize_particle_cloud(self, timestamp, xy_theta=None):
"""
Initialize the particle cloud.
Arguments
xy_theta: a triple consisting of the mean x, y, and theta (yaw) to initialize the
particle cloud around. If this input is omitted, the odometry will be used
Also check to see if we are attempting the robot kidnapping problem or are given an initial 2D pose
"""
if self.kidnap:
print("Kidnap Problem")
x_bound, y_bound = self.occupancy_field.get_obstacle_bounding_box()
x_particle = np.random.uniform(x_bound[0],
x_bound[1],
size=self.start_particles)
y_particle = np.random.uniform(y_bound[0],
y_bound[1],
size=self.start_particles)
theta_particle = np.deg2rad(
np.random.randint(0, 360, size=self.start_particles))
else:
print("Starting with Inital Position")
if xy_theta is None:
print("No Position Definied")
xy_theta = self.transform_helper.convert_pose_to_xy_and_theta(
self.odom_pose.pose)
x, y, theta = xy_theta
x_particle = np.random.normal(x, 0.25, size=self.start_particles)
y_particle = np.random.normal(y, 0.25, size=self.start_particles)
theta_particle = np.random.normal(theta,
0.001,
size=self.start_particles)
self.particle_cloud = [Particle(x_particle[i],\
y_particle[i],\
theta_particle[i]) \
for i in range(self.start_particles)]
def normalize_particles(self, particle_list):
""" Make sure the particle weights define a valid distribution (i.e. sum to 1.0) """
#print("Normalize Particles")
old_weights = [p.w for p in particle_list]
new_weights = []
for p in particle_list:
p.w = float(p.w) / sum(old_weights)
new_weights.append(p.w)
float_array = Float64MultiArray()
float_array.data = new_weights
self.weight_pub.publish(float_array)
def publish_particles(self, msg):
"""
Publishes particle poses on the map.
Uses Paul's default code at the moment, maybe later attempt to publish a visualization/MarkerArray
"""
particles_conv = []
for num, p in enumerate(self.particle_cloud):
particles_conv.append(p.as_pose())
self.particle_pub.publish(
PoseArray(header=Header(stamp=rospy.Time.now(),
frame_id=self.map_frame),
poses=particles_conv))
# self.marker_update("map", self.particle_cloud, False)
# self.viz.publish()
def scan_received(self, msg):
"""
All control flow happens here!
Special init case then goes into loop
"""
if not (self.initialized):
# wait for initialization to complete
return
# wait a little while to see if the transform becomes available. This fixes a race
# condition where the scan would arrive a little bit before the odom to base_link transform
# was updated.
# self.tf_listener.waitForTransform(self.base_frame, msg.header.frame_id, msg.header.stamp, rospy.Duration(0.5))
if not (self.tf_listener.canTransform(
self.base_frame, msg.header.frame_id, msg.header.stamp)):
# need to know how to transform the laser to the base frame
# this will be given by either Gazebo or neato_node
return
if not (self.tf_listener.canTransform(self.base_frame, self.odom_frame,
msg.header.stamp)):
# need to know how to transform between base and odometric frames
# this will eventually be published by either Gazebo or neato_node
return
# calculate pose of laser relative to the robot base
p = PoseStamped(
header=Header(stamp=rospy.Time(0), frame_id=msg.header.frame_id))
self.laser_pose = self.tf_listener.transformPose(self.base_frame, p)
# find out where the robot thinks it is based on its odometry
p = PoseStamped(header=Header(stamp=msg.header.stamp,
frame_id=self.base_frame),
pose=Pose())
self.odom_pose = self.tf_listener.transformPose(self.odom_frame, p)
# store the the odometry pose in a more convenient format (x,y,theta)
new_odom_xy_theta = self.transform_helper.convert_pose_to_xy_and_theta(
self.odom_pose.pose)
if not self.current_odom_xy_theta:
self.current_odom_xy_theta = new_odom_xy_theta
return
if not (self.particle_cloud):
print("Particle Cloud Empty")
# now that we have all of the necessary transforms we can update the particle cloud
self.initialize_particle_cloud(msg.header.stamp)
self.update_particles_with_laser(msg)
self.normalize_particles(self.particle_cloud)
self.update_robot_pose(msg.header.stamp)
self.resample_particles()
elif (math.fabs(new_odom_xy_theta[0] - self.current_odom_xy_theta[0]) >
self.d_thresh
or math.fabs(new_odom_xy_theta[1] -
self.current_odom_xy_theta[1]) > self.d_thresh
or math.fabs(new_odom_xy_theta[2] -
self.current_odom_xy_theta[2]) > self.a_thresh):
# we have moved far enough to do an update!
print("UPDATING PARTICLES")
self.update_particles_with_odom(msg) # update based on odometry
if self.last_projected_stable_scan:
last_projected_scan_timeshift = deepcopy(
self.last_projected_stable_scan)
last_projected_scan_timeshift.header.stamp = msg.header.stamp
self.scan_in_base_link = self.tf_listener.transformPointCloud(
"base_link", last_projected_scan_timeshift)
self.update_particles_with_laser(msg) # update based on laser scan
self.update_robot_pose(msg.header.stamp) # update robot's pose
self.resample_particles(
) # resample particles to focus on areas of high density
# publish particles (so things like rviz can see them)
self.publish_particles(msg)
def marker_update(self, frame_id, p_cloud, delete):
num = 0
if (delete):
self.markerArray.markers = []
else:
for p in p_cloud:
marker = Marker()
marker.header.frame_id = frame_id
marker.header.stamp = rospy.Time.now()
marker.ns = "my_namespace"
marker.id = num
marker.type = Marker.ARROW
marker.action = Marker.ADD
marker.pose = p.as_pose()
marker.pose.position.z = 0.5
marker.scale.x = 1.0
marker.scale.y = 0.1
marker.scale.z = 0.1
marker.color.a = 1.0 # Don't forget to set the alpha!
marker.color.r = 1.0
marker.color.g = 0.0
marker.color.b = 0.0
num += 1
self.markerArray.markers.append(marker)
@staticmethod
def sigmoid_function(value, max_output, min_output, middle, inc=1):
particle_difference = max_output - min_output
exponent = inc * (value - (middle / 2))
return int(particle_difference / (1 + np.exp(exponent)) + min_output)
class ParticleFilter:
""" The class that represents a Particle Filter ROS Node
Attributes list:
initialized: a Boolean flag to communicate to other class methods that initializaiton is complete
base_frame: the name of the robot base coordinate frame (should be "base_link" for most robots)
map_frame: the name of the map coordinate frame (should be "map" in most caPose(ses)
odom_frame: the name of the odometry coordinate frame (should be "odom" in most cases)
scan_topic: the name of the scan topic to listen to (should be "scan" in most cases)
n_particles: the number of particles in the filter
d_thresh: the amount of linear movement before triggering a filter update
a_thresh: the amount of angular movement before triggering a filter update
laser_max_distance: the maximum distance to an obstacle we should use in a likelihood calculation
pose_listener: a subscriber that listens for new approximate pose estimates (i.e. generated through the rviz GUI)
particle_pub: a publisher for the particle cloud
laser_subscriber: listens for new scan data on topic self.scan_topic
tf_listener: listener for coordinate transforms
tf_broadcaster: broadcaster for coordinate transforms
particle_cloud: a list of particles representing a probability distribution over robot poses
current_odom_xy_theta: the pose of the robot in the odometry frame when the last filter update was performed.
The pose is expressed as a list [x,y,theta] (where theta is the yaw)
map: the map we will be localizing ourselves in. The map should be of type nav_msgs/OccupancyGrid
"""
def __init__(self):
print "ParticleFilter initializing "
self.initialized = False # make sure we don't perform updates before everything is setup
rospy.init_node('comp_robo_project2') # tell roscore that we are creating a new node named "pf"
self.base_frame = "base_link" # the frame of the robot base
self.map_frame = "map" # the name of the map coordinate frame
self.odom_frame = "odom" # the name of the odometry coordinate frame
self.scan_topic = "scan" # the topic where we will get laser scans from
self.n_particles = 200 # the number of paporticles to use
self.d_thresh = 0.1 # the amount of linear movement before performing an update
self.a_thresh = math.pi/12 # the amount of angular movement before performing an update
self.laser_max_distance = 2.0 # maximum penalty to assess in the likelihood field model
# Setup pubs and subs
# pose_listener responds to selection of a new approximate robot location (for instance using rviz)
self.pose_listener = rospy.Subscriber("initialpose", PoseWithCovarianceStamped, self.update_initial_pose)
# publish the current particle cloud. This enables viewing particles in rviz.
self.particle_pub = rospy.Publisher("particlecloud", PoseArray)
self.pose_pub = rospy.Publisher("predictedPose", PoseArray)
self.scan_shift_pub = rospy.Publisher("scanShift", PoseArray)
# laser_subscriber listens for data from the lidar
self.laser_subscriber = rospy.Subscriber(self.scan_topic, LaserScan, self.scan_received)
# enable listening for and broadcasting coordinate transforms
self.tf_listener = TransformListener()
self.tf_broadcaster = TransformBroadcaster()
print "waiting for map server"
rospy.wait_for_service('static_map')
print "static_map service loaded"
static_map = rospy.ServiceProxy('static_map', GetMap)
worldMap = static_map()
if worldMap:
print "obtained map"
# for now we have commented out the occupancy field initialization until you can successfully fetch the map
self.occupancy_field = OccupancyField(worldMap.map)
self.initialized = True
print "ParticleFilter initialized"
def update_robot_pose(self):
""" Update the estimate of the robot's pose given the updated particles.
There are two logical methods for this:
(1): compute the mean pose (level 2)
(2): compute the most likely pose (i.e. the mode of the distribution) (level 1)
"""
highestWeight = 0
highestIndex = 0
weightsAndParticles = [] # array of tuples of the weight of each particle and the particle itself
for i in range(len(self.particle_cloud)):
weightsAndParticles.append((self.particle_cloud[i].w,self.particle_cloud[i]))
# Order by weights
sorted_by_first = sorted(weightsAndParticles, key=lambda tup: tup[0])[::-1]
# Select the the top third of particles with the hightes weights (probablilities)
topParticles = [i[1] for i in sorted_by_first][:int(self.n_particles*.3)]
# Average the top wighted particles to be the guessed position
self.robot_pose = self.averageHypos(topParticles)
def averageHypos(self, hypoList):
""" Averages the positions and angles of the input Particles
hypoList must be a list of Particles
returns Particle position info
"""
xList = []
yList = []
thetaList = []
if hypoList == [] or hypoList == None:
print "hypoList is invalid"
return Particle(x=0,y=0,theta=0,w=0).as_pose()
# Sort particles' characteristics in appropriate lists
for particle in hypoList:
xList.append(particle.x)
yList.append(particle.y)
thetaList.append(particle.theta)
# Average X and Y positions
averageX = sum(xList)/len(xList)
averageY = sum(yList)/len(yList)
# Average angles by decomposing vectors, averaging components, and converting back to an angle
unitXList = []
unitYList = []
for theta in thetaList:
unitXList.append(math.cos(theta))
unitYList.append(math.sin(theta))
averageUnitX = sum(unitXList)/len(unitXList)
averageUnitY = sum(unitYList)/len(unitYList)
averageTheta = (math.atan2(averageUnitY,averageUnitX)+(2*math.pi))%(2*math.pi)
return Particle(x=averageX,y=averageY,theta=averageTheta ,w=1.0).as_pose()
def update_particles_with_odom(self, msg):
""" Update the particles using the newly given odometry pose.
The function computes the value delta which is a tuple (x,y,theta)
that indicates the change in position and angle between the odometry
when the particles were last updated and the current odometry.
msg: this is not really needed to implement this, but is here just in case.
"""
new_odom_xy_theta = TransformHelpers.convert_pose_to_xy_and_theta(self.odom_pose.pose)
# compute the change in x,y,theta since our last update
if self.current_odom_xy_theta:
old_odom_xy_theta = self.current_odom_xy_theta
delta = (new_odom_xy_theta[0] - self.current_odom_xy_theta[0], new_odom_xy_theta[1] - self.current_odom_xy_theta[1], new_odom_xy_theta[2] - self.current_odom_xy_theta[2])
self.current_odom_xy_theta = new_odom_xy_theta
else:
self.current_odom_xy_theta = new_odom_xy_theta
return
# assumes map centered at 0,0
x_max_boundary = -self.occupancy_field.origin.position.x
x_min_boundary = self.occupancy_field.origin.position.x
y_max_boundary = -self.occupancy_field.origin.position.y
y_min_boundary = self.occupancy_field.origin.position.y
# Loops through particles to upsade with odom information
for i in range(len(self.particle_cloud)):
# Calculates amount of change for angle and X and Y position
tempDelta = self.rotatePositionChange(old_odom_xy_theta, delta, self.particle_cloud[i])
self.particle_cloud[i].x += tempDelta[0]
self.particle_cloud[i].y += tempDelta[1]
self.particle_cloud[i].theta += tempDelta[2]
# Accounts for angle wrapping
if self.particle_cloud[i].theta > (2*math.pi) or self.particle_cloud[i].theta < 0:
self.particle_cloud[i].theta = self.particle_cloud[i].theta%(2*math.pi)
#check map boundaries. Any particles no longer within map boundaries are moved to boundary
if self.particle_cloud[i].x > x_max_boundary:
self.particle_cloud[i].x = x_max_boundary
elif self.particle_cloud[i].x < x_min_boundary:
self.particle_cloud[i].x = x_min_boundary
if self.particle_cloud[i].y > y_max_boundary:
self.particle_cloud[i].y = y_max_boundary
elif self.particle_cloud[i].y < y_min_boundary:
self.particle_cloud[i].y = y_min_boundary
# For added difficulty: Implement sample_motion_odometry (Prob Rob p 136).
def rotatePositionChange(self,old_odom_xy_theta, delta, particle):
""" Determines the change in X and Y for each hypothesis based on its angle """
angle = particle.theta - old_odom_xy_theta[2]
newDeltaX = delta[0]*math.cos(angle) - delta[1]*math.sin(angle)
newDeltaY = delta[0]*math.sin(angle) + delta[1]*math.cos(angle)
return (newDeltaX,newDeltaY,delta[2])
def map_calc_range(self,x,y,theta):
""" Difficulty Level 3: implement a ray tracing likelihood model... Let me know if you are interested """
# TODO: nothing unless you want to try this alternate likelihood model
pass
def resample_particles(self):
""" Resample the particles according to the new particle weights.
The weights stored with each particle should define the probability that a particular
particle is selected in the resampling step. You may want to make use of the given helper
function draw_random_sample.
"""
weights = []
choices = []
probabilities = []
# Sort particle cloud info into appropriate arrays
for particle in self.particle_cloud:
choices.append(particle)
probabilities.append(particle.w)
# Only resample 2/3 of the original number of particles from current pool
numParticles = int(self.n_particles/3)*2
# Randomly draw particles from the current particle cloud biased towards points with higher weights
temp_particle_cloud = self.draw_random_sample(choices, probabilities, numParticles)
# Add uncertaintly/noise to all points
for particle in self.particle_cloud:
particle.x = particle.x + random.gauss(0, .1)
particle.y = particle.y + random.gauss(0, .1)
particle.theta = particle.theta + random.gauss(0, .4)
# Randomly pick the remaining 1/3 of particles randomly from known unoccupied cells of map, then
# combine with the 2/3 biasedly chosen earlier
self.particle_cloud = temp_particle_cloud + self.generateRandomParticles(self.n_particles - numParticles)
def generateRandomParticles(self, number):
""" Generates random particles from the unoccupied portion of the map
Returns array of random particles lengh of input number
"""
res = self.occupancy_field.map.info.resolution
temp_particle_cloud = []
unoccupied_cells = self.occupancy_field.unoccupied_cells
for i in range(number):
random_pt_index = int(random.uniform(0,len(unoccupied_cells)))
x = (unoccupied_cells[random_pt_index][0] ) * res + self.occupancy_field.origin.position.x
y = (unoccupied_cells[random_pt_index][1] ) * res + self.occupancy_field.origin.position.y
theta = random.uniform(0,2*math.pi)
rand_particle = Particle(x = x, y = y, theta = theta)
temp_particle_cloud.append(rand_particle)
return temp_particle_cloud
def update_particles_with_laser(self, msg):
""" Updates the particle weights in response to the scan contained in the msg """
# TODO: implement this
scanList = []
pointList = []
# create list of valid scans
for i in range(len(msg.ranges)):
if msg.ranges[i] < 6 and msg.ranges[i] >.2:
scanList.append((((float(i)/360.0)*2*math.pi),msg.ranges[i]))
# iterate through all particles
for particle in self.particle_cloud:
angleDif = (particle.theta - self.robot_pose.orientation.z+2*math.pi)%(2*math.pi)
#iterate through all valid scan points
errorList = []
for datum in scanList:
scanPosition = self.shiftScanToPoint(angleDif, datum, particle)
#print scanPosition
pointList.append(scanPosition)
dist = self.occupancy_field.get_closest_obstacle_distance(scanPosition[0],scanPosition[1])
errorList.append(math.pow(dist, 3))
particle.w = 1/(sum(errorList)/len(errorList))
print "errorAverage: " + str(particle.w)
#print "normError: " + str(self.particle_cloud)
# for particle in self.particle_cloud:
# weight = particle.w
# particle.w = 1-weight
#print "weight: " + str(particle.w)
weightList = []
#self.normalize_particles()
for i in range(len(self.particle_cloud)):
weightList.append(self.particle_cloud[i].w)
print weightList
self.normalize_particles()
#self.publish_shifted_scan(msg, pointList)
def shiftScanToPoint(self,angleDif, datum, particle):
#calculate real world position of datum
laserAngle = (datum[0]+angleDif + 2*math.pi)%(2*math.pi)
xDelta = datum[1]*math.cos(laserAngle)
yDelta = datum[1]*math.sin(laserAngle)
datumX = xDelta + particle.x
datumY = yDelta + particle.y
return (datumX,datumY)
@staticmethod
def angle_normalize(z):
""" convenience function to map an angle to the range [-pi,pi] """
return math.atan2(math.sin(z), math.cos(z))
@staticmethod
def angle_diff(a, b):
""" Calculates the difference between angle a and angle b (both should be in radians)
the difference is always based on the closest rotation from angle a to angle b
examples:
angle_diff(.1,.2) -> -.1
angle_diff(.1,2*math.pi-.1) -> .2
angle_diff(.1,.2+2*math.pi) -> -.1
"""
a = ParticleFilter.angle_normalize(a)
b = ParticleFilter.angle_normalize(b)
d1 = a-b
d2 = 2*math.pi - math.fabs(d1)
if d1 > 0:
d2 *= -1.0
if math.fabs(d1) < math.fabs(d2):
return d1
else:
return d2
@staticmethod
def weighted_values(values, probabilities, size):
""" Return a random sample of size elements form the set values with the specified probabilities
values: the values to sample from (numpy.ndarray)
probabilities: the probability of selecting each element in values (numpy.ndarray)
size: the number of samples
"""
bins = np.add.accumulate(probabilities)
return values[np.digitize(random_sample(size), bins)]
@staticmethod
def draw_random_sample(choices, probabilities, n):
""" Return a random sample of n elements from the set choices with the specified probabilities
choices: the values to sample from represented as a list
probabilities: the probability of selecting each element in choices represented as a list
n: the number of samples
"""
values = np.array(range(len(choices)))
probs = np.array(probabilities)
bins = np.add.accumulate(probs)
inds = values[np.digitize(random_sample(n), bins)]
samples = []
for i in inds:
samples.append(deepcopy(choices[int(i)]))
return samples
def update_initial_pose(self, msg):
""" Callback function to handle re-initializing the particle filter based on a pose estimate.
These pose estimates could be generated by another ROS Node or could come from the rviz GUI """
xy_theta = TransformHelpers.convert_pose_to_xy_and_theta(msg.pose.pose)
self.initialize_particle_cloud(xy_theta)
self.fix_map_to_odom_transform(msg)
def initialize_particle_cloud(self, xy_theta=None):
""" Initialize the particle cloud.
Arguments
xy_theta: a triple consisting of the mean x, y, and theta (yaw) to initialize the
particle cloud around. If this input is ommitted, the odometry will be used """
print "initializing particle cloud"
self.particle_cloud = []
unoccupied_cells = self.occupancy_field.unoccupied_cells
# When no guess given, initialize paricle cloud by random points in known unocupied portion of map
if xy_theta == None:
self.particle_cloud = generateRandomParticles(self, self.n_particles)
else:
print "guess given"
for i in range(self.n_particles):
x = random.gauss(xy_theta[0], 1)
y = random.gauss(xy_theta[1], 1)
theta = (random.gauss(xy_theta[2], 1.5))
rand_particle = Particle(x = x, y = y, theta = theta)
self.particle_cloud.append(rand_particle)
# Get map characteristics to generate points randomly in that realm. Assume
self.particle_pub.publish()
self.update_robot_pose()
print "particle cloud initialized"
def normalize_particles(self):
""" Make sure the particle weights define a valid distribution (i.e. sum to 1.0)"""
numParticles = len(self.particle_cloud)
weightArray = np.empty([numParticles, 1])
for i in range(numParticles):
print
weightArray[i] = self.particle_cloud[i].w
print "weightArray: " + str(weightArray)
normWeights = weightArray/np.sum(weightArray)
print "normWeights: " + str(normWeights)
for i in range(numParticles):
self.particle_cloud[i].w = normWeights[i][0]
def publish_predicted_pose(self, msg):
# actually send the message so that we can view it in rviz
self.pose_pub.publish(PoseArray(header=Header(stamp=rospy.Time.now(),frame_id=self.map_frame),poses=[self.robot_pose]))
def publish_particles(self, msg):
particles_conv = []
for p in self.particle_cloud:
particles_conv.append(p.as_pose())
# actually send the message so that we can view it in rviz
self.particle_pub.publish(PoseArray(header=Header(stamp=rospy.Time.now(),frame_id=self.map_frame),poses=particles_conv))
def publish_shifted_scan(self, msg, pointList):
particles_conv = []
for point in pointList:
particles_conv.append(Pose(position=Point(x=point[0],y=point[1],z=0), orientation=Quaternion(x=0, y=0, z=0, w=0)))
# actually send the message so that we can view it in rviz
self.scan_shift_pub.publish(PoseArray(header=Header(stamp=rospy.Time.now(),frame_id=self.map_frame),poses=particles_conv))
def scan_received(self, msg):
""" This is the default logic for what to do when processing scan data. Feel free to modify this, however,
I hope it will provide a good guide. The input msg is an object of type sensor_msgs/LaserScan """
#print "scan received"
if not(self.initialized):
# wait for initialization to complete
print "not initialized"
return
if not(self.tf_listener.canTransform(self.base_frame,msg.header.frame_id,msg.header.stamp)):
# need to know how to transform the laser to the base frame
# this will be given by either Gazebo or neato_node
return
if not(self.tf_listener.canTransform(self.base_frame,self.odom_frame,msg.header.stamp)):
# need to know how to transform between base and odometric frames
# this will eventually be published by either Gazebo or neato_node
return
# calculate pose of laser relative ot the robot base
p = PoseStamped(header=Header(stamp=rospy.Time(0),frame_id=msg.header.frame_id))
self.laser_pose = self.tf_listener.transformPose(self.base_frame,p)
# find out where the robot thinks it is based on its odometry
p = PoseStamped(header=Header(stamp=msg.header.stamp,frame_id=self.base_frame), pose=Pose())
self.odom_pose = self.tf_listener.transformPose(self.odom_frame, p)
# store the the odometry pose in a more convenient format (x,y,theta)
new_odom_xy_theta = TransformHelpers.convert_pose_to_xy_and_theta(self.odom_pose.pose)
try:
self.particle_cloud
except:
# now that we have all of the necessary transforms we can update the particle cloud
self.initialize_particle_cloud()
# cache the last odometric pose so we can only update our particle filter if we move more than self.d_thresh or self.a_thresh
self.current_odom_xy_theta = new_odom_xy_theta
# update our map to odom transform now that the particles are initialized
self.fix_map_to_odom_transform(msg)
if (math.fabs(new_odom_xy_theta[0] - self.current_odom_xy_theta[0]) > self.d_thresh or
math.fabs(new_odom_xy_theta[1] - self.current_odom_xy_theta[1]) > self.d_thresh or
math.fabs(new_odom_xy_theta[2] - self.current_odom_xy_theta[2]) > self.a_thresh):
# we have moved far enough to do an update!
self.update_particles_with_odom(msg) # update based on odometry
self.update_particles_with_laser(msg) # update based on laser scan
self.update_robot_pose()
self.publish_particles(msg) # update robot's pose
self.resample_particles() # resample particles to focus on areas of high density
self.fix_map_to_odom_transform(msg) # update map to odom transform now that we have new particles
# publish particles (so things like rviz can see them)
#self.publish_particles(msg)
self.publish_predicted_pose(msg)
def fix_map_to_odom_transform(self, msg):
""" Super tricky code to properly update map to odom transform... do not modify this... Difficulty level infinity. """
(translation, rotation) = TransformHelpers.convert_pose_inverse_transform(self.robot_pose)
p = PoseStamped(pose=TransformHelpers.convert_translation_rotation_to_pose(translation,rotation),header=Header(stamp=msg.header.stamp,frame_id=self.base_frame))
self.odom_to_map = self.tf_listener.transformPose(self.odom_frame, p)
(self.translation, self.rotation) = TransformHelpers.convert_pose_inverse_transform(self.odom_to_map.pose)
def broadcast_last_transform(self):
""" Make sure that we are always broadcasting the last map to odom transformation.
This is necessary so things like move_base can work properly. """
if not(hasattr(self,'translation') and hasattr(self,'rotation')):
return
self.tf_broadcaster.sendTransform(self.translation, self.rotation, rospy.get_rostime(), self.odom_frame, self.map_frame)
