def feedback_odom_callback(self, msg):
if not self.odom:
return
self.feedback_odom = msg
with self.lock:
# check distribution accuracy
nearest_odom = copy.deepcopy(self.odom)
nearest_dt = (self.feedback_odom.header.stamp - self.odom.header.stamp).to_sec()
for hist in self.odom_history: # get neaerest odom from feedback_odom referencing timestamp
dt = (self.feedback_odom.header.stamp - hist.header.stamp).to_sec()
if abs(dt) < abs(nearest_dt):
nearest_dt = dt
nearest_odom = copy.deepcopy(hist)
# get approximate pose at feedback_odom timestamp (probably it is past) of nearest_odom
global_nearest_odom_twist = transform_local_twist_to_global(nearest_odom.pose.pose, nearest_odom.twist.twist)
nearest_odom.pose.pose = update_pose(nearest_odom.pose.pose, global_nearest_odom_twist, nearest_dt)
global_nearest_odom_twist_covariance = transform_local_twist_covariance_to_global(nearest_odom.pose.pose, nearest_odom.twist.covariance)
nearest_odom.pose.covariance = update_pose_covariance(nearest_odom.pose.covariance, global_nearest_odom_twist_covariance, nearest_dt)
enable_feedback = self.check_covariance(nearest_odom) or self.check_distribution_difference(nearest_odom, self.feedback_odom) or self.check_feedback_time()
# update feedback_odom to approximate odom at current timestamp using previsous velocities
if enable_feedback:
rospy.loginfo("%s: Feedback enabled.", rospy.get_name())
# adjust timestamp of self.feedback_odom to current self.odom
for hist in self.odom_history:
dt = (hist.header.stamp - self.feedback_odom.header.stamp).to_sec()
if dt > 0.0:
# update pose and twist according to the history
self.update_twist(self.feedback_odom.twist, hist.twist)
global_hist_twist = transform_local_twist_to_global(hist.pose.pose, hist.twist.twist)
self.feedback_odom.pose.pose = update_pose(self.feedback_odom.pose.pose, global_hist_twist, dt) # update feedback_odom according to twist of hist
# update covariance
self.feedback_odom.twist.covariance = hist.twist.covariance
global_hist_twist_covariance = transform_local_twist_covariance_to_global(self.feedback_odom.pose.pose, hist.twist.covariance)
self.feedback_odom.pose.covariance = update_pose_covariance(self.feedback_odom.pose.covariance, global_hist_twist_covariance, dt)
self.feedback_odom.header.stamp = hist.header.stamp
dt = (self.odom.header.stamp - self.feedback_odom.header.stamp).to_sec()
global_feedback_odom_twist = transform_local_twist_to_global(self.feedback_odom.pose.pose, self.feedback_odom.twist.twist)
self.feedback_odom.pose.pose = update_pose(self.feedback_odom.pose.pose, global_feedback_odom_twist, dt)
global_feedback_odom_twist_covariance = transform_local_twist_covariance_to_global(self.feedback_odom.pose.pose, self.feedback_odom.twist.covariance)
self.feedback_odom.pose.covariance = update_pose_covariance(self.feedback_odom.pose.covariance, global_feedback_odom_twist_covariance, dt)
self.feedback_odom.header.stamp = self.odom.header.stamp
# integrate feedback_odom and current odom
new_odom_pose, new_odom_cov = self.calculate_mean_and_covariance(self.odom.pose, self.feedback_odom.pose)
# update self.odom according to the result of integration
quat = tf.transformations.quaternion_from_euler(*new_odom_pose[3:6])
self.odom.pose.pose = Pose(Point(*new_odom_pose[0:3]), Quaternion(*quat))
self.odom.pose.covariance = new_odom_cov
self.prev_feedback_time = self.odom.header.stamp
self.odom_history = []
# update offset
new_pose_homogeneous_matrix = make_homogeneous_matrix([self.odom.pose.pose.position.x, self.odom.pose.pose.position.y, self.odom.pose.pose.position.z],
[self.odom.pose.pose.orientation.x, self.odom.pose.pose.orientation.y, self.odom.pose.pose.orientation.z, self.odom.pose.pose.orientation.w])
source_homogeneous_matrix = make_homogeneous_matrix([self.source_odom.pose.pose.position.x, self.source_odom.pose.pose.position.y, self.source_odom.pose.pose.position.z],
[self.source_odom.pose.pose.orientation.x, self.source_odom.pose.pose.orientation.y, self.source_odom.pose.pose.orientation.z, self.source_odom.pose.pose.orientation.w])
# Hnew = Hold * T -> T = Hold^-1 * Hnew
self.offset_homogeneous_matrix = numpy.dot(numpy.linalg.inv(source_homogeneous_matrix), new_pose_homogeneous_matrix) # self.odom.header.stamp is assumed to be same as self.source_odom.header.stamp
def update_pose_with_covariance(self, pose_with_covariance,
twist_with_covariance, dt):
global_twist_with_covariance = self.transform_twist_with_covariance_to_global(
pose_with_covariance, twist_with_covariance)
ret_pose = update_pose(pose_with_covariance.pose,
global_twist_with_covariance.twist, dt)
ret_pose_cov = update_pose_covariance(
pose_with_covariance.covariance,
global_twist_with_covariance.covariance, dt)
return PoseWithCovariance(ret_pose, ret_pose_cov)
def sampling(self, particles, source_odom):
global_twist_with_covariance = self.transform_twist_with_covariance_to_global(
source_odom.pose, source_odom.twist)
sampled_velocities = self.state_transition_probability_rvs(
global_twist_with_covariance.twist,
global_twist_with_covariance.covariance
) # make sampeld velocity at once because multivariate_normal calculates invert matrix and it is slow
dt = (source_odom.header.stamp - self.odom.header.stamp).to_sec()
return [
update_pose(prt, Twist(Vector3(*vel[0:3]), Vector3(*vel[3:6])), dt)
for prt, vel in zip(particles, sampled_velocities)
]
def feedback_odom_callback(self, msg):
if not self.odom:
return
self.feedback_odom = msg
with self.lock:
# check distribution accuracy
nearest_odom = copy.deepcopy(self.odom)
nearest_dt = (self.feedback_odom.header.stamp -
self.odom.header.stamp).to_sec()
for hist in self.odom_history: # get neaerest odom from feedback_odom referencing timestamp
dt = (self.feedback_odom.header.stamp -
hist.header.stamp).to_sec()
if abs(dt) < abs(nearest_dt):
nearest_dt = dt
nearest_odom = copy.deepcopy(hist)
# get approximate pose at feedback_odom timestamp (probably it is past) of nearest_odom
global_nearest_odom_twist = transform_local_twist_to_global(
nearest_odom.pose.pose, nearest_odom.twist.twist)
nearest_odom.pose.pose = update_pose(nearest_odom.pose.pose,
global_nearest_odom_twist,
nearest_dt)
global_nearest_odom_twist_covariance = transform_local_twist_covariance_to_global(
nearest_odom.pose.pose, nearest_odom.twist.covariance)
nearest_odom.pose.covariance = update_pose_covariance(
nearest_odom.pose.covariance,
global_nearest_odom_twist_covariance, nearest_dt)
enable_feedback = self.check_covariance(
nearest_odom) or self.check_distribution_difference(
nearest_odom,
self.feedback_odom) or self.check_feedback_time()
# update feedback_odom to approximate odom at current timestamp using previsous velocities
if enable_feedback:
rospy.loginfo("%s: Feedback enabled.", rospy.get_name())
# adjust timestamp of self.feedback_odom to current self.odom
for hist in self.odom_history:
dt = (hist.header.stamp -
self.feedback_odom.header.stamp).to_sec()
if dt > 0.0:
# update pose and twist according to the history
self.update_twist(self.feedback_odom.twist, hist.twist)
global_hist_twist = transform_local_twist_to_global(
hist.pose.pose, hist.twist.twist)
self.feedback_odom.pose.pose = update_pose(
self.feedback_odom.pose.pose, global_hist_twist, dt
) # update feedback_odom according to twist of hist
# update covariance
self.feedback_odom.twist.covariance = hist.twist.covariance
global_hist_twist_covariance = transform_local_twist_covariance_to_global(
self.feedback_odom.pose.pose,
hist.twist.covariance)
self.feedback_odom.pose.covariance = update_pose_covariance(
self.feedback_odom.pose.covariance,
global_hist_twist_covariance, dt)
self.feedback_odom.header.stamp = hist.header.stamp
dt = (self.odom.header.stamp -
self.feedback_odom.header.stamp).to_sec()
global_feedback_odom_twist = transform_local_twist_to_global(
self.feedback_odom.pose.pose,
self.feedback_odom.twist.twist)
self.feedback_odom.pose.pose = update_pose(
self.feedback_odom.pose.pose, global_feedback_odom_twist,
dt)
global_feedback_odom_twist_covariance = transform_local_twist_covariance_to_global(
self.feedback_odom.pose.pose,
self.feedback_odom.twist.covariance)
self.feedback_odom.pose.covariance = update_pose_covariance(
self.feedback_odom.pose.covariance,
global_feedback_odom_twist_covariance, dt)
self.feedback_odom.header.stamp = self.odom.header.stamp
# integrate feedback_odom and current odom
new_odom_pose, new_odom_cov = self.calculate_mean_and_covariance(
self.odom.pose, self.feedback_odom.pose)
# update self.odom according to the result of integration
quat = tf.transformations.quaternion_from_euler(
*new_odom_pose[3:6])
self.odom.pose.pose = Pose(Point(*new_odom_pose[0:3]),
Quaternion(*quat))
self.odom.pose.covariance = new_odom_cov
self.prev_feedback_time = self.odom.header.stamp
self.odom_history = []
# update offset
new_pose_homogeneous_matrix = make_homogeneous_matrix([
self.odom.pose.pose.position.x,
self.odom.pose.pose.position.y,
self.odom.pose.pose.position.z
], [
self.odom.pose.pose.orientation.x,
self.odom.pose.pose.orientation.y,
self.odom.pose.pose.orientation.z,
self.odom.pose.pose.orientation.w
])
source_homogeneous_matrix = make_homogeneous_matrix([
self.source_odom.pose.pose.position.x,
self.source_odom.pose.pose.position.y,
self.source_odom.pose.pose.position.z
], [
self.source_odom.pose.pose.orientation.x,
self.source_odom.pose.pose.orientation.y,
self.source_odom.pose.pose.orientation.z,
self.source_odom.pose.pose.orientation.w
])
# Hnew = Hold * T -> T = Hold^-1 * Hnew
self.offset_homogeneous_matrix = numpy.dot(
numpy.linalg.inv(source_homogeneous_matrix),
new_pose_homogeneous_matrix
) # self.odom.header.stamp is assumed to be same as self.source_odom.header.stamp
def sampling(self, particles, source_odom):
global_twist_with_covariance = self.transform_twist_with_covariance_to_global(source_odom.pose, source_odom.twist)
sampled_velocities = self.state_transition_probability_rvs(global_twist_with_covariance.twist, global_twist_with_covariance.covariance) # make sampeld velocity at once because multivariate_normal calculates invert matrix and it is slow
dt = (source_odom.header.stamp - self.odom.header.stamp).to_sec()
return [update_pose(prt, Twist(Vector3(*vel[0:3]), Vector3(*vel[3:6])), dt) for prt, vel in zip(particles, sampled_velocities)]
def update_pose_with_covariance(self, pose_with_covariance, twist_with_covariance, dt):
global_twist_with_covariance = self.transform_twist_with_covariance_to_global(pose_with_covariance, twist_with_covariance)
ret_pose = update_pose(pose_with_covariance.pose, global_twist_with_covariance.twist, dt)
ret_pose_cov = update_pose_covariance(pose_with_covariance.covariance, global_twist_with_covariance.covariance, dt)
return PoseWithCovariance(ret_pose, ret_pose_cov)
