def get_same_timestamp_poses(poses_a, poses_b, add_orientations=True, abs_tol=0):
trimmed_poses_a = poses.Poses(poses_a.pose_type, poses_a.topic)
trimmed_poses_b = poses.Poses(poses_b.pose_type, poses_b.topic)
poses_a_size = len(poses_a.times)
poses_b_size = len(poses_b.times)
a_index = 0
b_index = 0
# Increment a and b index as needed. Add same timestamped poses to trimmed poses containers
while (a_index < poses_a_size) and (b_index < poses_b_size):
a_time = poses_a.times[a_index]
b_time = poses_b.times[b_index]
if (np.isclose(a_time, b_time, rtol=0, atol=abs_tol)):
trimmed_poses_a.positions.add_vector3d(poses_a.positions.get_vector3d(a_index))
trimmed_poses_a.times.append(poses_a.times[a_index])
trimmed_poses_b.positions.add_vector3d(poses_b.positions.get_vector3d(b_index))
trimmed_poses_b.times.append(poses_b.times[b_index])
if add_orientations:
trimmed_poses_a.orientations.add_euler(poses_a.orientations.get_euler(a_index))
trimmed_poses_b.orientations.add_euler(poses_b.orientations.get_euler(b_index))
a_index += 1
b_index += 1
elif (a_time < b_time):
a_index += 1
else:
b_index += 1
return trimmed_poses_a, trimmed_poses_b
def get_same_timestamp_poses(poses_a, poses_b):
trimmed_poses_a = poses.Poses(poses_a.pose_type, poses_a.topic)
trimmed_poses_b = poses.Poses(poses_b.pose_type, poses_b.topic)
poses_a_size = len(poses_a.times)
poses_b_size = len(poses_b.times)
a_index = 0
b_index = 0
# Increment a and b index as needed. Add same timestamped poses to trimmed poses containers
while (a_index < poses_a_size) and (b_index < poses_b_size):
a_time = poses_a.times[a_index]
b_time = poses_b.times[b_index]
if (a_time == b_time):
trimmed_poses_a.positions.add_vector3d(
poses_a.positions.get_vector3d(a_index))
# trimmed_poses_a.orientations.add_vector3d(poses_a.orientations.get_vector3d(a_index))
trimmed_poses_a.times.append(poses_a.times[a_index])
trimmed_poses_b.positions.add_vector3d(
poses_b.positions.get_vector3d(b_index))
# trimmed_poses_b.orientations.add_vector3d(poses_b.orientations.get_vector3d(b_index))
trimmed_poses_b.times.append(poses_b.times[b_index])
a_index += 1
b_index += 1
elif (a_time < b_time):
a_index += 1
else:
b_index += 1
return trimmed_poses_a, trimmed_poses_b
def create_plots(bagfile, output_pdf_file, output_csv_file='results.csv'):
bag = rosbag.Bag(bagfile)
bag_start_time = bag.get_start_time()
has_imu_augmented_graph_localization_state = has_topic(bag, '/gnc/ekf')
has_imu_bias_tester_poses = has_topic(bag, '/imu_bias_tester/pose')
sparse_mapping_poses = poses.Poses('Sparse Mapping',
'/sparse_mapping/pose')
ar_tag_poses = poses.Poses('AR Tag', '/ar_tag/pose')
imu_bias_tester_poses = poses.Poses('Imu Bias Tester',
'/imu_bias_tester/pose')
vec_of_poses = [sparse_mapping_poses, ar_tag_poses, imu_bias_tester_poses]
load_pose_msgs(vec_of_poses, bag, bag_start_time)
graph_localization_states = loc_states.LocStates('Graph Localization',
'/graph_loc/state')
imu_augmented_graph_localization_states = loc_states.LocStates(
'Imu Augmented Graph Localization', '/gnc/ekf')
vec_of_loc_states = [
graph_localization_states, imu_augmented_graph_localization_states
]
load_loc_state_msgs(vec_of_loc_states, bag, bag_start_time)
imu_bias_tester_velocities = velocities.Velocities(
'Imu Bias Tester', '/imu_bias_tester/velocity')
load_velocity_msgs(imu_bias_tester_velocities, bag, bag_start_time)
bag.close()
with PdfPages(output_pdf_file) as pdf:
add_graph_plots(pdf, sparse_mapping_poses, ar_tag_poses,
graph_localization_states,
imu_augmented_graph_localization_states)
if has_imu_bias_tester_poses:
add_imu_bias_tester_poses(pdf, imu_bias_tester_poses,
sparse_mapping_poses)
add_imu_bias_tester_velocities(pdf, imu_bias_tester_velocities)
if has_imu_augmented_graph_localization_state:
add_other_loc_plots(pdf, graph_localization_states,
imu_augmented_graph_localization_states,
sparse_mapping_poses, ar_tag_poses)
else:
add_other_loc_plots(pdf, graph_localization_states,
graph_localization_states)
plot_loc_state_stats(pdf, graph_localization_states,
sparse_mapping_poses, output_csv_file)
plot_loc_state_stats(pdf, imu_augmented_graph_localization_states,
sparse_mapping_poses, output_csv_file,
'imu_augmented_', 0.01)
if has_imu_bias_tester_poses:
plot_loc_state_stats(pdf, imu_bias_tester_poses,
sparse_mapping_poses, output_csv_file,
'imu_bias_tester_', 0.01, False)
def get_same_timestamp_poses(
poses_a,
poses_b,
add_orientations=True,
abs_tol=0,
rel_start_time=0,
rel_end_time=-1,
):
trimmed_poses_a = poses.Poses(poses_a.pose_type, poses_a.topic)
trimmed_poses_b = poses.Poses(poses_b.pose_type, poses_b.topic)
poses_a_size = len(poses_a.times)
poses_b_size = len(poses_b.times)
a_index = 0
b_index = 0
# Increment a and b index as needed. Add same timestamped poses to trimmed poses containers
while (a_index < poses_a_size) and (b_index < poses_b_size):
a_time = poses_a.times[a_index]
b_time = poses_b.times[b_index]
# Check if times are within given start and end time bounds
if a_time < rel_start_time:
a_index += 1
continue
if b_time < rel_start_time:
b_index += 1
continue
# rel_end_time less than zero indicates no bound on end time
if rel_end_time >= 0:
if a_time > rel_end_time or b_time > rel_end_time:
break
if np.isclose(a_time, b_time, rtol=0, atol=abs_tol):
trimmed_poses_a.positions.add_vector3d(
poses_a.positions.get_vector3d(a_index))
trimmed_poses_a.times.append(poses_a.times[a_index])
trimmed_poses_b.positions.add_vector3d(
poses_b.positions.get_vector3d(b_index))
trimmed_poses_b.times.append(poses_b.times[b_index])
if add_orientations:
trimmed_poses_a.orientations.add_euler(
poses_a.orientations.get_euler(a_index))
trimmed_poses_b.orientations.add_euler(
poses_b.orientations.get_euler(b_index))
a_index += 1
b_index += 1
elif a_time < b_time:
a_index += 1
else:
b_index += 1
return trimmed_poses_a, trimmed_poses_b
def make_poses(times, xs, ys, zs):
new_poses = poses.Poses("", "")
new_poses.times = times
new_poses.positions.xs = xs
new_poses.positions.ys = ys
new_poses.positions.zs = zs
return new_poses
def integrate_velocities(localization_states):
delta_times = [j - i for i, j in zip(localization_states.times[:-1], localization_states.times[1:])]
# Make sure times are same length as velocities, ignore last velocity
delta_times.append(0)
integrated_positions = poses.Poses('Integrated Graph Velocities', '')
# TODO(rsoussan): Integrate angular velocities?
# TODO(rsoussan): central difference instead?
x_increments = [velocity * delta_t for velocity, delta_t in zip(localization_states.velocities.xs, delta_times)]
cumulative_x_increments = np.cumsum(x_increments)
integrated_positions.positions.xs = [
localization_states.positions.xs[0] + cumulative_x_increment for cumulative_x_increment in cumulative_x_increments
]
y_increments = [velocity * delta_t for velocity, delta_t in zip(localization_states.velocities.ys, delta_times)]
cumulative_y_increments = np.cumsum(y_increments)
integrated_positions.positions.ys = [
localization_states.positions.ys[0] + cumulative_y_increment for cumulative_y_increment in cumulative_y_increments
]
z_increments = [velocity * delta_t for velocity, delta_t in zip(localization_states.velocities.zs, delta_times)]
cumulative_z_increments = np.cumsum(z_increments)
integrated_positions.positions.zs = [
localization_states.positions.zs[0] + cumulative_z_increment for cumulative_z_increment in cumulative_z_increments
]
# Add start positions
integrated_positions.positions.xs.insert(0, localization_states.positions.xs[0])
integrated_positions.positions.ys.insert(0, localization_states.positions.ys[0])
integrated_positions.positions.zs.insert(0, localization_states.positions.zs[0])
# Remove last elements (no timestamp for these)
del integrated_positions.positions.xs[-1]
del integrated_positions.positions.ys[-1]
del integrated_positions.positions.zs[-1]
integrated_positions.times = localization_states.times
return integrated_positions
def make_absolute_poses_from_relative_poses(absolute_poses, relative_poses,
name):
starting_relative_time = relative_poses.times[0]
np_times = np.array(absolute_poses.times)
closest_index = np.argmin(np.abs(np_times - starting_relative_time))
start_pose = absolute_poses.pose(closest_index)
new_pose = start_pose
new_poses_list = [start_pose]
new_poses_times = [absolute_poses.times[closest_index]]
for index in range(len(relative_poses.times)):
relative_pose = relative_poses.pose(index)
new_pose = new_pose * relative_pose
new_poses_list.append(new_pose)
new_poses_times.append(relative_poses.times[index])
new_poses = poses.Poses(name, "")
new_poses.init_from_poses(new_poses_list, new_poses_times)
return new_poses
def add_increments_to_absolute_pose(
x_increments,
y_increments,
z_increments,
starting_x,
starting_y,
starting_z,
times,
poses_name="Increment Poses",
):
integrated_positions = poses.Poses(poses_name, "")
cumulative_x_increments = np.cumsum(x_increments)
integrated_positions.positions.xs = [
starting_x + cumulative_x_increment
for cumulative_x_increment in cumulative_x_increments
]
cumulative_y_increments = np.cumsum(y_increments)
integrated_positions.positions.ys = [
starting_y + cumulative_y_increment
for cumulative_y_increment in cumulative_y_increments
]
cumulative_z_increments = np.cumsum(z_increments)
integrated_positions.positions.zs = [
starting_z + cumulative_z_increment
for cumulative_z_increment in cumulative_z_increments
]
# Add start positions
integrated_positions.positions.xs.insert(0, starting_x)
integrated_positions.positions.ys.insert(0, starting_y)
integrated_positions.positions.zs.insert(0, starting_z)
# Remove last elements (no timestamp for these)
del integrated_positions.positions.xs[-1]
del integrated_positions.positions.ys[-1]
del integrated_positions.positions.zs[-1]
integrated_positions.times = times
return integrated_positions
def create_plots(
bagfile,
output_pdf_file,
output_csv_file="results.csv",
groundtruth_bagfile=None,
rmse_rel_start_time=0,
rmse_rel_end_time=-1,
):
bag = rosbag.Bag(bagfile)
groundtruth_bag = rosbag.Bag(
groundtruth_bagfile) if groundtruth_bagfile else bag
bag_start_time = bag.get_start_time()
has_imu_augmented_graph_localization_state = has_topic(bag, "/gnc/ekf")
has_imu_bias_tester_poses = has_topic(bag, "/imu_bias_tester/pose")
sparse_mapping_poses = poses.Poses("Sparse Mapping",
"/sparse_mapping/pose")
ar_tag_poses = poses.Poses("AR Tag", "/ar_tag/pose")
imu_bias_tester_poses = poses.Poses("Imu Bias Tester",
"/imu_bias_tester/pose")
vec_of_poses = [ar_tag_poses, imu_bias_tester_poses]
load_pose_msgs(vec_of_poses, bag, bag_start_time)
has_depth_odom = has_topic(bag, "/loc/depth/odom")
depth_odom_relative_poses = poses.Poses("Depth Odom", "/loc/depth/odom")
load_odometry_msgs([depth_odom_relative_poses], bag, bag_start_time)
groundtruth_vec_of_poses = [sparse_mapping_poses]
load_pose_msgs(groundtruth_vec_of_poses, groundtruth_bag, bag_start_time)
graph_localization_states = loc_states.LocStates("Graph Localization",
"/graph_loc/state")
imu_augmented_graph_localization_states = loc_states.LocStates(
"Imu Augmented Graph Localization", "/gnc/ekf")
vec_of_loc_states = [
graph_localization_states,
imu_augmented_graph_localization_states,
]
load_loc_state_msgs(vec_of_loc_states, bag, bag_start_time)
imu_bias_tester_velocities = velocities.Velocities(
"Imu Bias Tester", "/imu_bias_tester/velocity")
load_velocity_msgs(imu_bias_tester_velocities, bag, bag_start_time)
bag.close()
with PdfPages(output_pdf_file) as pdf:
add_graph_plots(
pdf,
sparse_mapping_poses,
ar_tag_poses,
graph_localization_states,
imu_augmented_graph_localization_states,
)
if has_imu_bias_tester_poses:
add_imu_bias_tester_poses(pdf, imu_bias_tester_poses,
sparse_mapping_poses)
add_imu_bias_tester_velocities(pdf, imu_bias_tester_velocities)
if has_imu_augmented_graph_localization_state:
add_other_loc_plots(
pdf,
graph_localization_states,
imu_augmented_graph_localization_states,
sparse_mapping_poses,
ar_tag_poses,
)
else:
add_other_loc_plots(pdf, graph_localization_states,
graph_localization_states)
if has_depth_odom:
depth_odom_poses = utilities.make_absolute_poses_from_relative_poses(
sparse_mapping_poses, depth_odom_relative_poses,
"Depth Odometry")
plot_poses(pdf, depth_odom_poses, sparse_mapping_poses,
ar_tag_poses)
# Note that for absolute time difference tolerance depth images and groudtruth use different sensor data
# and therefore have less similar timestamps. This timestamp difference reduces the accuracy of depth odometry
# groundtruth comparison.
plot_loc_state_stats(
pdf,
depth_odom_poses,
sparse_mapping_poses,
output_csv_file,
"depth_odometry_",
0.01,
False,
rmse_rel_start_time=rmse_rel_start_time,
rmse_rel_end_time=rmse_rel_end_time,
)
plot_covariances(
pdf,
depth_odom_relative_poses.times,
depth_odom_relative_poses.covariances.position,
"Depth Odometry Position",
)
plot_covariances(
pdf,
depth_odom_relative_poses.times,
depth_odom_relative_poses.covariances.orientation,
"Depth Odometry Orientation",
)
plot_loc_state_stats(
pdf,
graph_localization_states,
sparse_mapping_poses,
output_csv_file,
rmse_rel_start_time=rmse_rel_start_time,
rmse_rel_end_time=rmse_rel_end_time,
)
plot_loc_state_stats(
pdf,
imu_augmented_graph_localization_states,
sparse_mapping_poses,
output_csv_file,
"imu_augmented_",
0.01,
rmse_rel_start_time=rmse_rel_start_time,
rmse_rel_end_time=rmse_rel_end_time,
)
if has_imu_bias_tester_poses:
plot_loc_state_stats(
pdf,
imu_bias_tester_poses,
sparse_mapping_poses,
output_csv_file,
"imu_bias_tester_",
0.01,
False,
rmse_rel_start_time=rmse_rel_start_time,
rmse_rel_end_time=rmse_rel_end_time,
)
def create_plots(
bagfile,
output_pdf_file,
output_csv_file="results.csv",
groundtruth_bagfile=None,
rmse_rel_start_time=0,
rmse_rel_end_time=-1,
):
bag = rosbag.Bag(bagfile)
groundtruth_bag = rosbag.Bag(
groundtruth_bagfile) if groundtruth_bagfile else bag
bag_start_time = bag.get_start_time()
has_imu_augmented_graph_localization_state = has_topic(bag, "/gnc/ekf")
has_imu_bias_tester_poses = has_topic(bag, "/imu_bias_tester/pose")
sparse_mapping_poses = poses.Poses("Sparse Mapping",
"/sparse_mapping/pose")
ar_tag_poses = poses.Poses("AR Tag", "/ar_tag/pose")
imu_bias_tester_poses = poses.Poses("Imu Bias Tester",
"/imu_bias_tester/pose")
vec_of_poses = [ar_tag_poses, imu_bias_tester_poses]
load_pose_msgs(vec_of_poses, bag, bag_start_time)
groundtruth_vec_of_poses = [sparse_mapping_poses]
load_pose_msgs(groundtruth_vec_of_poses, groundtruth_bag, bag_start_time)
graph_localization_states = loc_states.LocStates("Graph Localization",
"/graph_loc/state")
imu_augmented_graph_localization_states = loc_states.LocStates(
"Imu Augmented Graph Localization", "/gnc/ekf")
vec_of_loc_states = [
graph_localization_states,
imu_augmented_graph_localization_states,
]
load_loc_state_msgs(vec_of_loc_states, bag, bag_start_time)
imu_bias_tester_velocities = velocities.Velocities(
"Imu Bias Tester", "/imu_bias_tester/velocity")
load_velocity_msgs(imu_bias_tester_velocities, bag, bag_start_time)
bag.close()
with PdfPages(output_pdf_file) as pdf:
add_graph_plots(
pdf,
sparse_mapping_poses,
ar_tag_poses,
graph_localization_states,
imu_augmented_graph_localization_states,
)
if has_imu_bias_tester_poses:
add_imu_bias_tester_poses(pdf, imu_bias_tester_poses,
sparse_mapping_poses)
add_imu_bias_tester_velocities(pdf, imu_bias_tester_velocities)
if has_imu_augmented_graph_localization_state:
add_other_loc_plots(
pdf,
graph_localization_states,
imu_augmented_graph_localization_states,
sparse_mapping_poses,
ar_tag_poses,
)
else:
add_other_loc_plots(pdf, graph_localization_states,
graph_localization_states)
plot_loc_state_stats(
pdf,
graph_localization_states,
sparse_mapping_poses,
output_csv_file,
rmse_rel_start_time=rmse_rel_start_time,
rmse_rel_end_time=rmse_rel_end_time,
)
plot_loc_state_stats(
pdf,
imu_augmented_graph_localization_states,
sparse_mapping_poses,
output_csv_file,
"imu_augmented_",
0.01,
rmse_rel_start_time=rmse_rel_start_time,
rmse_rel_end_time=rmse_rel_end_time,
)
if has_imu_bias_tester_poses:
plot_loc_state_stats(
pdf,
imu_bias_tester_poses,
sparse_mapping_poses,
output_csv_file,
"imu_bias_tester_",
0.01,
False,
rmse_rel_start_time=rmse_rel_start_time,
rmse_rel_end_time=rmse_rel_end_time,
)