def __init__(self):
#
rospy.init_node("ParticleFilter")
self.lidar_sub = rospy.Subscriber("/scan",LaserScan, self.lidar_callback)
self.odom_sub = rospy.Subscriber("/odom",Odometry, self.odom_callback)
self.all_particles_pub = rospy.Publisher("/visualization_particles", MarkerArray, queue_size=10)
self.init_particles_pub = rospy.Publisher("/visualization_init",MarkerArray,queue_size=10)
self.initial_estimate_sub = rospy.Subscriber("/initialpose",PoseWithCovarianceStamped,self.pose_estimate_callback)
self.occupancy_field = OccupancyField()
self.number_of_particles = 20
self.particles = np.ones([3,self.number_of_particles], dtype=np.float)
self.weights = np.ones(self.number_of_particles, dtype=np.float)
pos_std_dev = 0.25
ori_std_dev = 25 * math.pi / 180
self.initial_std_dev = np.array([[pos_std_dev, pos_std_dev, ori_std_dev]]).T
self.lidar_std_dev = 0.05
self.resample_threshold = 0.2
self.scan = None
self.prev_pose = None
self.delta_pose = None
self.initial_pose_estimate = None
self.pose = None
rospy.loginfo("Initialized")
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 __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.initial_uncertainty_xy = 1 # Amplitute factor of initial x and y uncertainty
self.initial_uncertainty_theta = 0.5 # Amplitude factor of initial theta uncertainty
self.variance_scale = 0.15 # Scaling term for variance effect on resampling
self.n_particles_average = 20 # Number of particles to average for pose update
self.linear_var_thresh = 0.05 # Maximum confidence along x/y (meters)
self.angular_var_thresh = 0.2 # Maximum confidence along theta (radians)
# self.resample_noise_xy = 0.1 # Amplitude factor of resample x and y noise
# self.resample_noise_theta = 0.1 # Amplitude factor of resample theta noise
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)
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 custom particle array messge type
self.particle_viz_pub = rospy.Publisher("weighted_particlecloud", ParticleArray, 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()
self.initialized = True
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
# If these are set any lower, the transform will timeout
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
self.num_particles = 150
self.particle_movement_noise = 0.1
self.sensor_variance = 0.05
# 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",
MarkerArray,
queue_size=10)
# Publishes points (places where robot is guessing a wall is)
self.point_publisher = rospy.Publisher(
'/visualization_messages/Marker', Marker, 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
occ_map = self.get_map_from_server()
self.occupancy_field = OccupancyField(occ_map)
self.initialized = True
# Initialize at 0,0
self.robot_pose = Pose()
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 __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 = 1000 # the number of particles to use
self.n_angles = 20 # the number of angles to use from the range scan. Value contained in the interval [1,360]
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.std_x = 1 # std of x
self.std_y = 1 # std of y
self.marker_multiplier = self.n_particles / 5
# 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("markerpub",
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
rospy.wait_for_service('static_map')
get_map = rospy.ServiceProxy('static_map', GetMap)
map = get_map().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 __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
self.sd_xy_theta = (0.5, 0.5, math.pi / 10)
# 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.mean_particle = Particle() # Save the most probable particle here
self.current_odom_xy_theta = [0, 0, 0]
# request the map from the map server, the map should be of type nav_msgs/OccupancyGrid
# TODONE: 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')
self.map = rospy.ServiceProxy('static_map', GetMap)().map
# DONE: uncommented the occupancy field initialization after you can successfully fetch the map
self.occupancy_field = OccupancyField(self.map)
self.initialized = True
def __init__(self):
# Initialize node and attributes
rospy.init_node("ParticleFilter")
# Subscribers
self.lidar_sub = rospy.Subscriber("/scan", LaserScan,
self.lidar_callback)
self.odom_sub = rospy.Subscriber("/odom", Odometry, self.odom_callback)
self.initial_estimate_sub = rospy.Subscriber(
"/initialpose", PoseWithCovarianceStamped,
self.pose_estimate_callback)
# publishers
self.all_particles_pub = rospy.Publisher("/visualization_particles",
MarkerArray,
queue_size=10)
self.init_particles_pub = rospy.Publisher("/visualization_init",
MarkerArray,
queue_size=10)
self.new_particles_pub = rospy.Publisher("/particlecloud",
PoseArray,
queue_size=10)
# constants
self.number_of_particles = 30
pos_std_dev = 0.25
ori_std_dev = 25 * math.pi / 180
self.initial_std_dev = np.array(
[[pos_std_dev, pos_std_dev, ori_std_dev]]).T
self.lidar_std_dev = 0.02
self.resample_threshold = 0.1
# changing attributes
self.particles = np.ones([3, self.number_of_particles], dtype=np.float)
self.weights = np.ones(self.number_of_particles, dtype=np.float)
self.odom_tf_time = 0
self.base_tf_time = 0
self.scan = None
self.prev_pose = None
self.delta_pose = None
self.initial_pose_estimate = None
self.pose = None
# helper classes
self.occupancy_field = OccupancyField()
self.tf_listener = TransformListener()
self.tf_broadcaster = TransformBroadcaster()
self.transform_helper = TFHelper()
rospy.loginfo("Initialized")
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))
def __init__(self, x=0.0, y=0.0, theta=0.0, w=1.0):
'''
x: x-coord of the particle relative to map
y: y-coord of the particle relative to map
theta: angle of the particle relative to map
w: weight of particle
'''
self.initialized = False # dont init anything until im ready
rospy.init_node('localizer')
self.base_frame = "base_link"
self.map_frame = "map"
self.odom_frame = "odom"
self.scan_topic = "stable_scan"
self.particles = 500 #based on talking to Paul
self.linear_thresh = 0.25 #linear movement before updating
self.angular_thresh = math.pi/10 #angular movement before updating
self.particle_cloud = []
self.current_odom_xy_theta = []
# From Paul's occupancy_field.py code
rospy.wait_for_service('static_map')
map_server = rospy.ServiceProxy('static_map', GetMap)
map = map_server().map
# initialize occupancy field
self.occupancy_field = OccupancyField(map)
print("Oh yeah we're occupied")
self.initialized = True
self.x = x
self.y = y
self.theta = theta
self.w = w
def __init__(self):
rospy.init_node('pf')
# create instances of two helper objects that are provided to you
# as part of the project
self.occupancy_field = OccupancyField()
self.tfh = TFHelper()
self.ros_boss = RosBoss()
# publisher for the particle cloud for visualizing in rviz.
self.particle_pub = rospy.Publisher("particlecloud",
PoseArray,
queue_size=10)
# pose_listener responds to selection of a new approximate robot
# location (for instance using rviz)
rospy.Subscriber("/odom", Odometry, self.update_initial_pose)
self.current_particles = {}
self.max_particle_number = 100
self.map_width = self.occupancy_field.map.info.width
self.map_height = self.occupancy_field.map.info.height
self.map_origin = (self.occupancy_field.map.info.origin.position.x,
self.occupancy_field.map.info.origin.position.y)
self.map_resolution = self.occupancy_field.map.info.resolution
self.particle_viz = ParticlesMarker()
self.last_time = time.time()
def __init__(self, top_particle_pub, particle_cloud_pub, particle_cloud_marker_pub):
# # pose_listener responds to selection of a new approximate robot
# # location (for instance using rviz)
# rospy.Subscriber("initialpose",
# PoseWithCovarianceStamped,
# self.update_initial_pose)
self.top_particle_pub = top_particle_pub
self.particle_cloud_pub = particle_cloud_pub
self.particle_cloud_marker_pub = particle_cloud_marker_pub
# create instances of two helper objects that are provided to you
# as part of the project
self.occupancy_field = OccupancyField()
self.transform_helper = TFHelper()
self.particles = []
self.markerArray = MarkerArray()
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 __init__(self):
self.occupancy_field = OccupancyField()
self.locations = {
} # location to be stored as tuple(x, y, theta): confidence
self.confidence_func = lambda new_confidence, old_confidence: (
new_confidence + old_confidence) / 2
self.pc_pub = rospy.Publisher('/particlecloud',
PoseArray,
queue_size=10)
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 __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 __init__(self):
rospy.init_node("sensor_likelihood_test")
self.occupancy_field = OccupancyField()
self.tf_helper = TFHelper()
self.latest_scan_ranges = []
rospy.Subscriber('/scan', LaserScan, self.read_sensor)
self.odom_poses = PoseArray()
self.odom_poses.header.frame_id = "odom"
self.particle_pose_pub = rospy.Publisher('/particle_pose_array',
PoseArray,
queue_size=10)
self.odom_pose_pub = rospy.Publisher('odom_pose',
PoseArray,
queue_size=10)
self.marker_pub = rospy.Publisher('/visualization_marker_array',
MarkerArray,
queue_size=10)
self.p_distrib = ParticleDistribution()
self.init_particles()
# self.p = Particle(x=0, y=0, theta=0, weight=1)
self.particle_poses = PoseArray()
self.particle_poses.header.frame_id = "map"
self.last_odom_pose = PoseStamped()
self.last_odom_pose.header.frame_id = "odom"
self.last_odom_pose.header.stamp = rospy.Time(0)
self.base_link_pose = PoseStamped()
self.base_link_pose.header.frame_id = "base_link"
self.base_link_pose.header.stamp = rospy.Time(0)
self.counter = 0
self.is_first = True
# pose_listener responds to selection of a new approximate robot
# location (for instance using rviz)
rospy.Subscriber("initialpose", PoseWithCovarianceStamped,
self.update_initial_pose)
def __init__(self):
rospy.init_node('pf')
self.particle_publisher = rospy.Publisher("particlecloud",
PoseArray,
queue_size=10)
self.occupancy_field = OccupancyField()
self.transform_helper = TFHelper()
self.particle_manager = ParticleManager()
self.sensor_manager = SensorManager()
self.particle_manager.init_particles(self.occupancy_field)
self.scanDistance = 0.2
self.scanAngle = 0.5
self.moved = (0, 0)
def __init__(self):
rospy.init_node('pf')
# pose_listener responds to selection of a new approximate robot
# location (for instance using rviz)
rospy.Subscriber("initialpose",
PoseWithCovarianceStamped,
self.update_initial_pose)
# publisher for the particle cloud for visualizing in rviz.
self.particle_pub = rospy.Publisher("particlecloud",
PoseArray,
queue_size=10)
# create instances of two helper objects that are provided to you
# as part of the project
self.occupancy_field = OccupancyField()
self.transform_helper = TFHelper()
self.particles = Particles()
self.particles.initialize_particles()
self.ranges = []
def __init__(self):
print('Initializing')
self.initialized = False # make sure we don't perform updates before everything is setup
rospy.init_node('localizer')
self.pf = ParticleFilter()
self.base_frame = "base_link" # Robot base frame
self.map_frame = "map" # Map coord frame
self.odom_frame = "odom" # Odom coord frame
self.scan_topic = "scan" # Laser scan topic
self.linear_threshold = 0.1 # the amount of linear movement before performing an update
self.angular_threshold = math.pi / 10 # the amount of angular movement before performing an update
self.max_dist = 2.0 # maximum penalty to assess in the likelihood field model
self.odom_pose = PoseStamped()
self.robot_pose = Pose()
self.robot_pose = Pose()
self.scan_sub = rospy.Subscriber('/scan', LaserScan, self.process_scan)
# init pf
# subscribers and publisher
self.odom_sub = rospy.Subscriber("/odom", Odometry,
self.odom_particle_updater)
rospy.Subscriber("initialpose", PoseWithCovarianceStamped,
self.pose_updater)
# enable listening for and broadcasting coord transforms
self.tf_listener = TransformListener()
self.tf_broadcaster = TransformBroadcaster()
self.occupancy_field = OccupancyField()
self.transform_helper = TFHelper()
self.current_odom_xy_theta = []
print("initialization complete")
self.initialized = True
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 __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 __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
class ParticleMatcher(object):
#map is a 2D array passed with a singular data value on whether or not there is
#something in the coordinate
def __init__(self):
#self.map_ = map_
self.OF = OccupancyField()
def d2p(self, d, eps=1e-3):
# return d.max() - d + eps # linear-mode
# return (1.0 / (d + eps)) # inverse-mode
kp, kx = 0.5, 1.0 # configure as p=50% match at 1.0m distance
k = (-np.log(kp) / kx)
return np.exp(-k * d)
def match(self, particle_list, scan, min_num=5):
if (len(scan) <= min_num):
return None
#scan is a list of [angle, theta]
min_dist = np.min(scan[:, 1])
#print('ps', particle_list)
#dist = [self.OF.get_closest_obstacle_distance(p[0], p[1]) for p in particle_list]
ps = particle_list
dist = self.OF.get_closest_obstacle_distance(ps[:, 0], ps[:, 1])
dist = np.asarray(dist, dtype=np.float32)
dist[np.isnan(
dist
)] = np.inf # WARNING : setting to np.inf doesn't work for linear-mode d2p.
cost = np.abs(np.subtract(dist, min_dist))
#print('dist stats : min {} max {} std {}'.format(dist.min(),dist.max(),dist.std()))
#cost = np.abs(np.subtract(dist, min_dist))
#cost[np.isnan(cost)] = 0 # set nan cost to zero to prevent artifacts
#weight = cost.max() - cost + 1e-3
weight = self.d2p(cost)
#weight = 1.0 / cost
# TODO : determine inverse vs. linear cost performance comparison
#weight[np.isnan(dist)] = 0 # set nan weight to zero to make sure it doesn't get sampled
#print('ws', weight)
return weight
def __init__(self):
rospy.init_node('pf')
real_robot = False
# create instances of two helper objects that are provided to you
# as part of the project
self.particle_filter = ParticleFilter()
self.occupancy_field = OccupancyField()
self.TFHelper = TFHelper()
self.sensor_model = sensor_model = SensorModel(
model_noise_rate=0.5,
odometry_noise_rate=0.15,
world_model=self.occupancy_field,
TFHelper=self.TFHelper)
self.position_delta = None # Pose, delta from current to previous odometry reading
self.last_scan = None # list of ranges
self.odom = None # Pose, current odometry reading
self.x_y_spread = 0.3 # Spread constant for x-y initialization of particles
self.z_spread = 0.2 # Spread constant for z initialization of particles
self.n_particles = 150 # number of particles
# pose_listener responds to selection of a new approximate robot
# location (for instance using rviz)
rospy.Subscriber("initialpose", PoseWithCovarianceStamped,
self.update_initial_pose)
rospy.Subscriber("odom", Odometry, self.update_position)
rospy.Subscriber("stable_scan", LaserScan, self.update_scan)
# publisher for the particle cloud for visualizing in rviz.
self.particle_pub = rospy.Publisher("particlecloud",
PoseArray,
queue_size=10)
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 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 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:
""" 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 = 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 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 __init__(self):
#self.map_ = map_
self.OF = OccupancyField()
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)
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
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 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)
def __init__(self, map_fname):
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 = "batman/base_link" # the frame of the robot base
self.map_frame = "map" # the name of the map coordinate frame
self.odom_frame = "batman/odom" # the name of the odometry coordinate frame
self.scan_topic = "batman/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 = 0.02 # guess for how inaccurate lidar readings are in meters
self.beacon_sigma = 2
# 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("bl_particlecloud",
PoseArray,
queue_size=10)
self.marker_pub = rospy.Publisher("markers",
MarkerArray,
queue_size=10)
self.pose_pub = rospy.Publisher("/batman/pose",
PoseStamped,
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.laser_pose = None
self.current_odom_xy_theta = []
self.cnt = 0
# 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.initialize_particle_cloud()
with open(map_fname, 'r') as map_file:
self.refpoints = [
yaml.load(x) for x in map_file.read().split('---')[:-1]
]
self.ref_vector_dimension = len(self.refpoints[0]['rssi'].keys())
self.beacons_subscriber = rospy.Subscriber(
'/batman/beacon_localization/distances/probabilistic',
BeaconsScan, self.beacons_received)
self.initialized = True
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, map_fname):
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 = "batman/base_link" # the frame of the robot base
self.map_frame = "map" # the name of the map coordinate frame
self.odom_frame = "batman/odom" # the name of the odometry coordinate frame
self.scan_topic = "batman/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 = 0.02 # guess for how inaccurate lidar readings are in meters
self.beacon_sigma = 2
# 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("bl_particlecloud",
PoseArray,
queue_size=10)
self.marker_pub = rospy.Publisher("markers",
MarkerArray,
queue_size=10)
self.pose_pub = rospy.Publisher("/batman/pose",
PoseStamped,
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.laser_pose = None
self.current_odom_xy_theta = []
self.cnt = 0
# 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.initialize_particle_cloud()
with open(map_fname, 'r') as map_file:
self.refpoints = [
yaml.load(x) for x in map_file.read().split('---')[:-1]
]
self.ref_vector_dimension = len(self.refpoints[0]['rssi'].keys())
self.beacons_subscriber = rospy.Subscriber(
'/batman/beacon_localization/distances/probabilistic',
BeaconsScan, self.beacons_received)
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]))
self.pose_pub.publish(
PoseStamped(header=Header(stamp=rospy.Time.now(), frame_id="map"),
pose=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:
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)
if not math.isnan(d):
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
self.update_robot_pose()
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 """
self.particle_cloud = []
nonoccupied_points = self.occupancy_field.free_cells[:]
for i in range(self.n_particles):
indx = random.randint(0, len(nonoccupied_points) - 1)
particle = Particle(nonoccupied_points[indx]['x'],
nonoccupied_points[indx]['y'],
random.random() * 360)
self.particle_cloud.append(particle)
del nonoccupied_points[indx]
self.normalize_particles()
self.update_robot_pose()
self.publish_particles()
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):
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.laser_pose:
self.tf_listener.waitForTransform(self.base_frame,
msg.header.frame_id,
rospy.Time(0),
rospy.Duration(10.0))
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)
self.recent_scan = msg
# 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.publish_particles()
def beacons_received(self, msg):
if not self.refpoints:
return
distribution = []
for point in self.refpoints:
squared_dist = 0.0
for beacon in msg.beacons:
squared_dist += (beacon.rssi - point['rssi'][beacon.name])**2
dist = np.sqrt(squared_dist)
measurement = {
'x': point['x'],
'y': point['y'],
'dist': dist,
}
distribution.append(measurement)
sorted_dist = sorted(distribution[:], key=lambda x: x['dist'])
avg_pose = {'x': 0, 'y': 0}
selected_ones = sorted_dist[:1]
for measurement in selected_ones:
avg_pose['x'] += measurement['x'] * measurement['dist']
avg_pose['y'] += measurement['y'] * measurement['dist']
avg_pose['x'] /= sum(item['dist'] for item in selected_ones)
avg_pose['y'] /= sum(item['dist'] for item in selected_ones)
# Make cloud great again
for particle in self.particle_cloud:
dist = math.sqrt((particle.x - avg_pose['x'])**2 +
(particle.y - avg_pose['y'])**2)
prob = math.exp((-dist**2) / (2 * self.beacon_sigma**2))
particle.w = prob
# Create new cloud
# 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() * 1.5
# # x => straight ahead
# x = radius * math.sin(other_theta) + avg_pose['x']
# y = radius * math.cos(other_theta) + avg_pose['y']
# particle = Particle(x, y, theta)
# self.particle_cloud.append(particle)
self.normalize_particles()
if self.cnt is 0:
self.resample_particles(
) # resample particles to focus on areas of high density
self.cnt = 5
self.cnt -= 1
self.update_particles_with_laser(self.recent_scan)
self.update_robot_pose()
self.publish_particles()
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.get_rostime(), self.odom_frame,
self.map_frame)
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 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)
