def point_plane_residual(x: np.ndarray, base_transform: np.ndarray,
pcd_source: o3d.geometry.PointCloud,
pcd_target: o3d.geometry.PointCloud,
kdtree_target: o3d.geometry.KDTreeFlann,
max_correspond_dist):
assert pcd_target.has_normals()
num_points = len(pcd_source.points)
points_source = np.asarray(pcd_source.points)
nearest_points = np.empty((num_points, 3), dtype=float)
nearest_normals = np.empty((num_points, 3), dtype=float)
points_target = np.asarray(pcd_target.points)
normals_target = np.asarray(pcd_target.normals)
for i in range(num_points):
k, idx, _ = kdtree_target.search_hybrid_vector_3d(
pcd_source.points[i], max_correspond_dist, 1)
if k == 1:
nearest_points[i, :] = points_target[idx[0], :]
nearest_normals[i, :] = normals_target[idx[0], :]
else:
nearest_points[i, :] = points_source[i, :]
nearest_normals[i, :] = np.array(
[0, 0, 0], dtype=float) # cause a zero residual
angle = x[0]
t1 = x[1]
t2 = x[2]
transform = rotation_matrix(angle, rotation_axis)
transform[:3, 3] = t1 * translation_axis1 + t2 * translation_axis2
transform = np.dot(transform, base_transform)
position_residuals = np.dot(transform[:3, :3],
points_source.T) + transform[:3, 3].reshape(
3, 1) - nearest_points.T
residuals = position_residuals * nearest_normals.T
residuals = np.sum(residuals, axis=0)
return residuals
def cicp(pcd_source: o3d.geometry.PointCloud,
pcd_target: o3d.geometry.PointCloud, base_transform: np.ndarray,
max_correspond_dist_coarse, max_correspond_dist_fine):
kdtree = o3d.geometry.KDTreeFlann(pcd_target)
if not pcd_target.has_normals():
o3d.estimate_normals(pcd_target,
search_param=o3d.KDTreeSearchParamHybrid(
radius=0.1, max_nn=30))
x0 = np.array([0, 0, 0], dtype=float)
result = least_squares(point_plane_residual,
x0,
jac='2-point',
method='trf',
loss='soft_l1',
args=(base_transform, pcd_source, pcd_target,
kdtree, max_correspond_dist_coarse))
x0 = result.x
result = least_squares(point_plane_residual,
x0,
jac='2-point',
method='trf',
loss='soft_l1',
args=(base_transform, pcd_source, pcd_target,
kdtree, max_correspond_dist_fine))
x = result.x
angle = x[0]
t1 = x[1]
t2 = x[2]
transform = rotation_matrix(angle, rotation_axis)
transform[:3, 3] = t1 * translation_axis1 + t2 * translation_axis2
transform = np.dot(transform, base_transform)
return transform, result