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# ----------------------------------------------------------------------------
# - Open3D: www.open3d.org -
# ----------------------------------------------------------------------------
# Copyright (c) 2018-2024 www.open3d.org
# SPDX-License-Identifier: MIT
# ----------------------------------------------------------------------------
# examples/python/reconstruction_system/refine_registration.py
import multiprocessing
import os
import sys
import numpy as np
import open3d as o3d
pyexample_path = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
sys.path.append(pyexample_path)
from open3d_example import join, get_file_list, write_poses_to_log, draw_registration_result_original_color
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from optimize_posegraph import optimize_posegraph_for_refined_scene
def update_posegraph_for_scene(s, t, transformation, information, odometry,
pose_graph):
if t == s + 1: # odometry case
odometry = np.dot(transformation, odometry)
odometry_inv = np.linalg.inv(odometry)
pose_graph.nodes.append(
o3d.pipelines.registration.PoseGraphNode(odometry_inv))
pose_graph.edges.append(
o3d.pipelines.registration.PoseGraphEdge(s,
t,
transformation,
information,
uncertain=False))
else: # loop closure case
pose_graph.edges.append(
o3d.pipelines.registration.PoseGraphEdge(s,
t,
transformation,
information,
uncertain=True))
return (odometry, pose_graph)
def multiscale_icp(source,
target,
voxel_size,
max_iter,
config,
init_transformation=np.identity(4)):
current_transformation = init_transformation
for i, scale in enumerate(range(len(max_iter))): # multi-scale approach
iter = max_iter[scale]
distance_threshold = config["voxel_size"] * 1.4
print("voxel_size {}".format(voxel_size[scale]))
source_down = source.voxel_down_sample(voxel_size[scale])
target_down = target.voxel_down_sample(voxel_size[scale])
if config["icp_method"] == "point_to_point":
result_icp = o3d.pipelines.registration.registration_icp(
source_down, target_down, distance_threshold,
current_transformation,
o3d.pipelines.registration.TransformationEstimationPointToPoint(
),
o3d.pipelines.registration.ICPConvergenceCriteria(
max_iteration=iter))
else:
source_down.estimate_normals(
o3d.geometry.KDTreeSearchParamHybrid(radius=voxel_size[scale] *
2.0,
max_nn=30))
target_down.estimate_normals(
o3d.geometry.KDTreeSearchParamHybrid(radius=voxel_size[scale] *
2.0,
max_nn=30))
if config["icp_method"] == "point_to_plane":
result_icp = o3d.pipelines.registration.registration_icp(
source_down, target_down, distance_threshold,
current_transformation,
o3d.pipelines.registration.
TransformationEstimationPointToPlane(),
o3d.pipelines.registration.ICPConvergenceCriteria(
max_iteration=iter))
if config["icp_method"] == "color":
# Colored ICP is sensitive to threshold.
# Fallback to preset distance threshold that works better.
# TODO: make it adjustable in the upgraded system.
result_icp = o3d.pipelines.registration.registration_colored_icp(
source_down, target_down, voxel_size[scale],
current_transformation,
o3d.pipelines.registration.
TransformationEstimationForColoredICP(),
o3d.pipelines.registration.ICPConvergenceCriteria(
relative_fitness=1e-6,
relative_rmse=1e-6,
max_iteration=iter))
if config["icp_method"] == "generalized":
result_icp = o3d.pipelines.registration.registration_generalized_icp(
source_down, target_down, distance_threshold,
current_transformation,
o3d.pipelines.registration.
TransformationEstimationForGeneralizedICP(),
o3d.pipelines.registration.ICPConvergenceCriteria(
relative_fitness=1e-6,
relative_rmse=1e-6,
max_iteration=iter))
current_transformation = result_icp.transformation
if i == len(max_iter) - 1:
information_matrix = o3d.pipelines.registration.get_information_matrix_from_point_clouds(
source_down, target_down, voxel_size[scale] * 1.4,
result_icp.transformation)
if config["debug_mode"]:
draw_registration_result_original_color(source, target,
result_icp.transformation)
return (result_icp.transformation, information_matrix)
def local_refinement(source, target, transformation_init, config):
voxel_size = config["voxel_size"]
(transformation, information) = \
multiscale_icp(
source, target,
[voxel_size, voxel_size/2.0, voxel_size/4.0], [50, 30, 14],
config, transformation_init)
return (transformation, information)
def register_point_cloud_pair(ply_file_names, s, t, transformation_init,
config):
print("reading %s ..." % ply_file_names[s])
source = o3d.io.read_point_cloud(ply_file_names[s])
print("reading %s ..." % ply_file_names[t])
target = o3d.io.read_point_cloud(ply_file_names[t])
(transformation, information) = \
local_refinement(source, target, transformation_init, config)
if config["debug_mode"]:
print(transformation)
print(information)
return (transformation, information)
# other types instead of class?
class matching_result:
def __init__(self, s, t, trans):
self.s = s
self.t = t
self.success = False
self.transformation = trans
self.infomation = np.identity(6)
def make_posegraph_for_refined_scene(ply_file_names, config):
pose_graph = o3d.io.read_pose_graph(
join(config["path_dataset"],
config["template_global_posegraph_optimized"]))
n_files = len(ply_file_names)
matching_results = {}
for edge in pose_graph.edges:
s = edge.source_node_id
t = edge.target_node_id
matching_results[s * n_files + t] = \
matching_result(s, t, edge.transformation)
if config["python_multi_threading"] is True:
os.environ['OMP_NUM_THREADS'] = '1'
max_workers = max(
1, min(multiprocessing.cpu_count() - 1, len(pose_graph.edges)))
mp_context = multiprocessing.get_context('spawn')
with mp_context.Pool(processes=max_workers) as pool:
args = [(ply_file_names, v.s, v.t, v.transformation, config)
for k, v in matching_results.items()]
results = pool.starmap(register_point_cloud_pair, args)
for i, r in enumerate(matching_results):
matching_results[r].transformation = results[i][0]
matching_results[r].information = results[i][1]
else:
for r in matching_results:
(matching_results[r].transformation,
matching_results[r].information) = \
register_point_cloud_pair(ply_file_names,
matching_results[r].s, matching_results[r].t,
matching_results[r].transformation, config)
pose_graph_new = o3d.pipelines.registration.PoseGraph()
odometry = np.identity(4)
pose_graph_new.nodes.append(
o3d.pipelines.registration.PoseGraphNode(odometry))
for r in matching_results:
(odometry, pose_graph_new) = update_posegraph_for_scene(
matching_results[r].s, matching_results[r].t,
matching_results[r].transformation, matching_results[r].information,
odometry, pose_graph_new)
print(pose_graph_new)
o3d.io.write_pose_graph(
join(config["path_dataset"], config["template_refined_posegraph"]),
pose_graph_new)
def run(config):
print("refine rough registration of fragments.")
o3d.utility.set_verbosity_level(o3d.utility.VerbosityLevel.Debug)
ply_file_names = get_file_list(
join(config["path_dataset"], config["folder_fragment"]), ".ply")
make_posegraph_for_refined_scene(ply_file_names, config)
optimize_posegraph_for_refined_scene(config["path_dataset"], config)
path_dataset = config['path_dataset']
n_fragments = len(ply_file_names)
# Save to trajectory
poses = []
pose_graph_fragment = o3d.io.read_pose_graph(
join(path_dataset, config["template_refined_posegraph_optimized"]))
for fragment_id in range(len(pose_graph_fragment.nodes)):
pose_graph_rgbd = o3d.io.read_pose_graph(
join(path_dataset,
config["template_fragment_posegraph_optimized"] % fragment_id))
for frame_id in range(len(pose_graph_rgbd.nodes)):
frame_id_abs = fragment_id * \
config['n_frames_per_fragment'] + frame_id
pose = np.dot(pose_graph_fragment.nodes[fragment_id].pose,
pose_graph_rgbd.nodes[frame_id].pose)
poses.append(pose)
traj_name = join(path_dataset, config["template_global_traj"])
write_poses_to_log(traj_name, poses)
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