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()
def rotate(self, angle):
self.theta = TFHelper.angle_normalize(self.theta + angle)