def get_icp_transform(world_points, iterations):
# Iterate assignment and estimation of trafo a few times.
# --->>> Implement your code here.
# You may use the following strategy:
# Start with the identity transform:
overall_trafo = (1.0, 1.0, 0.0, 0.0, 0.0)
# Then loop for j in xrange(iterations):
for j in xrange(iterations):
# Transform the world_points using the curent overall_trafo
# Call get_correspoinding_points_on_wall(...)
# Get the transformation
left, right = get_corresponding_points_on_wall(world_points)
# Determine transformation which is needed "on top of" the current
# overall_trafo: trafo = estimate_transform(...)
trafo = estimate_transform(left, right, fix_scale = True)
# Concatenate the found transformation with the current overall_trafo
# to obtain a new, 'combined' transformation to concatenate two similarities.
overall_trafo = concatenate_transform(trafo, overall_trafo)
# Correct the initial position using trafo. Also transform points.
if trafo:
world_points = [apply_transform(trafo, p) for p in world_points]
else:
world_points = []
# Return the final transformation.
return overall_trafo
def get_icp_transform(world_points, iterations):
# Iterate assignment and estimation of trafo a few times.
# --->>> Implement your code here.
# You may use the following strategy:
# Start with the identity transform:
overall_trafo = (1.0, 1.0, 0.0, 0.0, 0.0)
# Then loop for j in xrange(iterations):
for i in range(0, iterations):
# Transform the world_points using the curent overall_trafo
# (see 05_b on how to do this)
world_points = [apply_transform(overall_trafo, p) for p in world_points]
# Call get_correspoinding_points_on_wall(...)
left, right = get_corresponding_points_on_wall(world_points)
# Determine transformation which is needed "on top of" the current
# overall_trafo: trafo = estimate_transform(...)
trafo = estimate_transform(left, right, fix_scale = True)
# Concatenate the found transformation with the current overall_trafo
# to obtain a new, 'combined' transformation. You may use the function
# overall_trafo = concatenate_transform(trafo, overall_trafo)
# to concatenate two similarities.
# Note also that estimate_transform may return None.
if(trafo):
overall_trafo = concatenate_transform(trafo, overall_trafo)
#
# Return the final transformation.
return overall_trafo
def get_icp_transform(world_points, iterations):
# Iterate assignment and estimation of trafo a few times.
# --->>> Implement your code here.
# You may use the following strategy:
# Start with the identity transform:
overall_trafo = (1.0, 1.0, 0.0, 0.0, 0.0)
# Then loop for j in xrange(iterations):
# Transform the world_points using the curent overall_trafo
# (see 05_b on how to do this)
# Call get_correspoinding_points_on_wall(...)
# Determine transformation which is needed "on top of" the current
# overall_trafo: trafo = estimate_transform(...)
# Concatenate the found transformation with the current overall_trafo
# to obtain a new, 'combined' transformation. You may use the function
# overall_trafo = concatenate_transform(trafo, overall_trafo)
# to concatenate two similarities.
# Note also that estimate_transform may return None.
#
for i in range(iterations):
world_points_tr = [apply_transform(overall_trafo, p) for p in world_points]
left, right = get_corresponding_points_on_wall(world_points_tr)
trafo = estimate_transform(left, right, fix_scale = True)
if trafo:
overall_trafo = concatenate_transform(trafo, overall_trafo)
# Return the final transformation.
return overall_trafo
# Iterate over all positions.
out_file = open("estimate_wall_transform.txt", "w")
for i in range(len(logfile.scan_data)):
# Compute the new pose.
pose = filter_step(pose, logfile.motor_ticks[i], ticks_to_mm,
robot_width, scanner_displacement)
# Subsample points.
subsampled_points = get_subsampled_points(logfile.scan_data[i])
world_points = [
LegoLogfile.scanner_to_world(pose, c) for c in subsampled_points
]
# Get the transformation
left, right = get_corresponding_points_on_wall(world_points)
trafo = estimate_transform(left, right, fix_scale=True)
# Correct the initial position using trafo. Also transform points.
if trafo:
pose = correct_pose(pose, trafo)
world_points = [apply_transform(trafo, p) for p in world_points]
else:
world_points = []
# Write to file.
# The pose.
print("F %f %f %f" % pose, file=out_file)
# Write the scanner points and corresponding points.
write_cylinders(out_file, "W C", world_points)
# Iterate over all positions.
out_file = open("estimate_wall_transform.txt", "w")
for i in range(len(logfile.scan_data)):
# Compute the new pose.
pose = filter_step(pose, logfile.motor_ticks[i],
ticks_to_mm, robot_width,
scanner_displacement)
# Subsample points.
subsampled_points = get_subsampled_points(logfile.scan_data[i])
world_points = [LegoLogfile.scanner_to_world(pose, c)
for c in subsampled_points]
# Get the transformation
left, right = get_corresponding_points_on_wall(world_points)
trafo = estimate_transform(left, right, fix_scale = True)
# Correct the initial position using trafo. Also transform points.
if trafo:
pose = correct_pose(pose, trafo)
world_points = [apply_transform(trafo, p) for p in world_points]
else:
world_points = []
# Write to file.
# The pose.
out_file.write("F %f %f %f\n" % pose)
# Write the scanner points and corresponding points.
write_cylinders(out_file, "W C", world_points)
