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registration.py
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registration.py
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import os
import open3d as o3d
import numpy as np
def load_point_clouds(voxel_size=0.0, root="../softpool/pcds/gt/scene0011_00"):
pcds = []
features = []
for i in range(0, 3):
path = os.path.join(root, "0000000%d.pcd" % i)
pcd = o3d.io.read_point_cloud(path)
pcd_down = pcd.voxel_down_sample(voxel_size=voxel_size)
pcd_down.estimate_normals(
o3d.geometry.KDTreeSearchParamHybrid(
radius=voxel_size * 2, max_nn=30))
pcds.append(pcd_down)
feature_fpfh = o3d.registration.compute_fpfh_feature(
pcd_down,
o3d.geometry.KDTreeSearchParamHybrid(
radius=voxel_size * 5, max_nn=100))
features.append(feature_fpfh)
return pcds, features
def pairwise_registration(source, target, source_fpfh, target_fpfh,
max_correspondence_distance_coarse,
max_correspondence_distance_fine, voxel_size):
distance_threshold = voxel_size * 1.5
print(":: RANSAC registration on downsampled point clouds.")
print(" Since the downsampling voxel size is %.3f," % voxel_size)
print(" we use a liberal distance threshold %.3f." % distance_threshold)
result = o3d.registration.registration_ransac_based_on_feature_matching(
source, target, source_fpfh, target_fpfh, distance_threshold,
o3d.registration.TransformationEstimationPointToPoint(False), 4, [
o3d.registration.CorrespondenceCheckerBasedOnEdgeLength(0.9),
o3d.registration.CorrespondenceCheckerBasedOnDistance(
distance_threshold)
], o3d.registration.RANSACConvergenceCriteria(4000000, 5000))
print("Apply point-to-plane ICP")
icp_coarse = o3d.registration.registration_icp(
source,
target,
max_correspondence_distance_coarse,
result.transformation, #np.identity(4),
o3d.registration.TransformationEstimationPointToPlane())
icp_fine = o3d.registration.registration_icp(
source, target, max_correspondence_distance_fine,
icp_coarse.transformation,
o3d.registration.TransformationEstimationPointToPlane())
transformation_icp = icp_fine.transformation
information_icp = o3d.registration.get_information_matrix_from_point_clouds(
source, target, max_correspondence_distance_fine,
icp_fine.transformation)
return transformation_icp, information_icp
def full_registration(pcds, pcds_fpfh, max_correspondence_distance_coarse,
max_correspondence_distance_fine, voxel_size):
pose_graph = o3d.registration.PoseGraph()
odometry = np.identity(4)
pose_graph.nodes.append(o3d.registration.PoseGraphNode(odometry))
n_pcds = len(pcds)
for source_id in range(n_pcds):
for target_id in range(source_id + 1, n_pcds):
transformation_icp, information_icp = pairwise_registration(
pcds[source_id], pcds[target_id], pcds_fpfh[source_id],
pcds_fpfh[target_id], max_correspondence_distance_coarse,
max_correspondence_distance_fine, voxel_size)
print("Build o3d.registration.PoseGraph")
if target_id == source_id + 1: # odometry case
odometry = np.dot(transformation_icp, odometry)
pose_graph.nodes.append(
o3d.registration.PoseGraphNode(np.linalg.inv(odometry)))
pose_graph.edges.append(
o3d.registration.PoseGraphEdge(
source_id,
target_id,
transformation_icp,
information_icp,
uncertain=False))
else: # loop closure case
pose_graph.edges.append(
o3d.registration.PoseGraphEdge(
source_id,
target_id,
transformation_icp,
information_icp,
uncertain=True))
return pose_graph
def registrate(root_path):
voxel_size = 0.02
pcds_down, pcds_fpfh = load_point_clouds(
voxel_size=voxel_size, root=root_path)
# o3d.visualization.draw_geometries(pcds_down)
print("Full registration ...")
max_correspondence_distance_coarse = voxel_size * 15
max_correspondence_distance_fine = voxel_size * 1.5
"""
with o3d.utility.VerbosityContextManager(
o3d.utility.VerbosityLevel.Debug) as cm:
"""
pose_graph = full_registration(
pcds_down, pcds_fpfh, max_correspondence_distance_coarse,
max_correspondence_distance_fine, voxel_size)
print("Optimizing PoseGraph ...")
option = o3d.registration.GlobalOptimizationOption(
max_correspondence_distance=max_correspondence_distance_fine,
edge_prune_threshold=0.25,
reference_node=0)
"""
with o3d.utility.VerbosityContextManager(
o3d.utility.VerbosityLevel.Debug) as cm:
"""
o3d.registration.global_optimization(
pose_graph, o3d.registration.GlobalOptimizationLevenbergMarquardt(),
o3d.registration.GlobalOptimizationConvergenceCriteria(), option)
print("Transform points and display")
for point_id in range(len(pcds_down)):
print(pose_graph.nodes[point_id].pose)
pcds_down[point_id].transform(pose_graph.nodes[point_id].pose)
# o3d.visualization.draw_geometries(pcds_down)
print("Make a combined point cloud")
pcds, pcd_fpfh = load_point_clouds(voxel_size=voxel_size, root=root_path)
pcd_combined = o3d.geometry.PointCloud()
for point_id in range(len(pcds)):
pcds[point_id].transform(pose_graph.nodes[point_id].pose)
pcd_combined += pcds[point_id]
pcd_combined_down = pcd_combined.voxel_down_sample(voxel_size=voxel_size)
o3d.io.write_point_cloud(
os.path.join(root_path, "multiway_registration.pcd"),
pcd_combined_down)
# o3d.visualization.draw_geometries([pcd_combined_down])