How to use the open3d.registration function in open3d

def preprocess_point_cloud(pcd, config):
    voxel_size = config["voxel_size"]
    pcd_down = pcd.voxel_down_sample(voxel_size)
    pcd_down.estimate_normals(
        o3d.geometry.KDTreeSearchParamHybrid(radius=voxel_size * 2.0,
                                             max_nn=30))
    pcd_fpfh = o3d.registration.compute_fpfh_feature(
        pcd_down,
        o3d.geometry.KDTreeSearchParamHybrid(radius=voxel_size * 5.0,
                                             max_nn=100))
    return (pcd_down, pcd_fpfh)

def preprocess_point_cloud(pcd, voxel_size):
    print(":: Downsample with a voxel size %.3f." % voxel_size)
    pcd_down = pcd.voxel_down_sample(voxel_size)

    radius_normal = voxel_size * 2
    print(":: Estimate normal with search radius %.3f." % radius_normal)
    pcd_down.estimate_normals(
        o3d.geometry.KDTreeSearchParamHybrid(radius=radius_normal, max_nn=30))

    radius_feature = voxel_size * 5
    print(":: Compute FPFH feature with search radius %.3f." % radius_feature)
    pcd_fpfh = o3d.registration.compute_fpfh_feature(
        pcd_down,
        o3d.geometry.KDTreeSearchParamHybrid(radius=radius_feature, max_nn=100))
    return pcd_down, pcd_fpfh

result_icp = o3d.registration.registration_icp(
                    source_down, target_down, distance_threshold,
                    current_transformation,
                    o3d.registration.TransformationEstimationPointToPlane(),
                    o3d.registration.ICPConvergenceCriteria(max_iteration=iter))
            if config["icp_method"] == "color":
                result_icp = o3d.registration.registration_colored_icp(
                    source_down, target_down, voxel_size[scale],
                    current_transformation,
                    o3d.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.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)

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.registration.registration_icp(
                    source_down, target_down, distance_threshold,
                    current_transformation,
                    o3d.registration.TransformationEstimationPointToPlane(),
                    o3d.registration.ICPConvergenceCriteria(max_iteration=iter))
            if config["icp_method"] == "color":
                result_icp = o3d.registration.registration_colored_icp(
                    source_down, target_down, voxel_size[scale],
                    current_transformation,
                    o3d.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.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 compute(self, data):
        pcd = o3.geometry.PointCloud()
        pcd.points = o3.utility.Vector3dVector(data)
        self.estimate_normals(pcd)
        fpfh = o3.registration.compute_fpfh_feature(pcd, self._param_feature)
        return fpfh.data.T

def icp_registration(source, target, voxel_size, transform_init, max_iterations):
    # See http://www.open3d.org/docs/release/tutorial/Basic/icp_registration.html#icp-registration
    threshold = 8.0 * voxel_size
    reg_p2p = o3d.registration.registration_icp(
        source, target, threshold, transform_init,
        o3d.registration.TransformationEstimationPointToPoint(),
        o3d.registration.ICPConvergenceCriteria(max_iteration=max_iterations))
    return reg_p2p

def register_point_cloud_fpfh(source, target, source_fpfh, target_fpfh, config):
    distance_threshold = config["voxel_size"] * 1.4
    if config["global_registration"] == "fgr":
        result = o3d.registration.registration_fast_based_on_feature_matching(
            source, target, source_fpfh, target_fpfh,
            o3d.registration.FastGlobalRegistrationOption(
                maximum_correspondence_distance=distance_threshold))
    if config["global_registration"] == "ransac":
        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, 500))
    if (result.transformation.trace() == 4.0):
        return (False, np.identity(4), np.zeros((6, 6)))
    information = o3d.registration.get_information_matrix_from_point_clouds(
        source, target, distance_threshold, result.transformation)
    if information[5, 5] / min(len(source.points), len(target.points)) < 0.3:
        return (False, np.identity(4), np.zeros((6, 6)))
    return (True, result.transformation, information)

def update_posegrph_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.registration.PoseGraphNode(odometry_inv))
        pose_graph.edges.append(
            o3d.registration.PoseGraphEdge(s,
                                           t,
                                           transformation,
                                           information,
                                           uncertain=False))
    else:  # loop closure case
        pose_graph.edges.append(
            o3d.registration.PoseGraphEdge(s,
                                           t,
                                           transformation,
                                           information,
                                           uncertain=True))
    return (odometry, pose_graph)

def execute_fast_global_registration(source_down, target_down, source_fpfh,
                                     target_fpfh, voxel_size):
    distance_threshold = voxel_size * 0.5
    print(":: Apply fast global registration with distance threshold %.3f" \
            % distance_threshold)
    result = o3d.registration.registration_fast_based_on_feature_matching(
        source_down, target_down, source_fpfh, target_fpfh,
        o3d.registration.FastGlobalRegistrationOption(
            maximum_correspondence_distance=distance_threshold))
    return result