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Add rotation and translation epsilon in python interface #91

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Sep 21, 2024
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Add rotation and translation epsilon in python interface #91

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30 changes: 30 additions & 0 deletions src/python/align.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -33,6 +33,8 @@ void define_align(py::module& m) {
double max_correspondence_distance,
int num_threads,
int max_iterations,
double rotation_epsilon,
double translation_epsilon,
bool verbose) {
if (target_points.cols() != 3 && target_points.cols() != 4) {
std::cerr << "target_points must be Nx3 or Nx4" << std::endl;
Expand Down Expand Up @@ -62,6 +64,8 @@ void define_align(py::module& m) {
setting.max_correspondence_distance = max_correspondence_distance;
setting.num_threads = num_threads;
setting.max_iterations = max_iterations;
setting.rotation_eps = rotation_epsilon;
setting.translation_eps = translation_epsilon;
setting.verbose = verbose;

std::vector<Eigen::Vector4d> target(target_points.rows());
Expand Down Expand Up @@ -97,6 +101,8 @@ void define_align(py::module& m) {
py::arg("max_correspondence_distance") = 1.0,
py::arg("num_threads") = 1,
py::arg("max_iterations") = 20,
py::arg("rotation_epsilon") = 0.1 * M_PI / 180.0,
py::arg("translation_epsilon") = 1e-3,
py::arg("verbose") = false,
R"pbdoc(
Align two point clouds using various ICP-like algorithms.
Expand Down Expand Up @@ -125,6 +131,10 @@ void define_align(py::module& m) {
Number of threads to use for parallel processing.
max_iterations : int = 20
Maximum number of iterations for the optimization algorithm.
rotation_epsilon: double = 0.1 * M_PI / 180.0
Convergence criteria for rotation change
translation_epsilon: double = 1e-3
Convergence criteria for transformation change
verbose : bool = False
If True, print debug information during the optimization process.

Expand All @@ -146,6 +156,8 @@ void define_align(py::module& m) {
double max_correspondence_distance,
int num_threads,
int max_iterations,
double rotation_epsilon,
double translation_epsilon,
bool verbose) {
RegistrationSetting setting;
if (registration_type == "ICP") {
Expand All @@ -161,6 +173,8 @@ void define_align(py::module& m) {
setting.max_correspondence_distance = max_correspondence_distance;
setting.num_threads = num_threads;
setting.max_iterations = max_iterations;
setting.rotation_eps = rotation_epsilon;
setting.translation_eps = translation_epsilon;
setting.verbose = verbose;

if (target_tree == nullptr) {
Expand All @@ -176,6 +190,8 @@ void define_align(py::module& m) {
py::arg("max_correspondence_distance") = 1.0,
py::arg("num_threads") = 1,
py::arg("max_iterations") = 20,
py::arg("rotation_epsilon") = 0.1 * M_PI / 180.0,
py::arg("translation_epsilon") = 1e-3,
py::arg("verbose") = false,
R"pbdoc(
Align two point clouds using specified ICP-like algorithms, utilizing point cloud and KD-tree inputs.
Expand All @@ -200,6 +216,10 @@ void define_align(py::module& m) {
Number of threads to use for computation.
max_iterations : int = 20
Maximum number of iterations for the optimization algorithm.
rotation_epsilon: double = 0.1 * M_PI / 180.0
Convergence criteria for rotation change
translation_epsilon: double = 1e-3
Convergence criteria for transformation change
verbose : bool = False
If True, print debug information during the optimization process.

Expand All @@ -219,11 +239,15 @@ void define_align(py::module& m) {
double max_correspondence_distance,
int num_threads,
int max_iterations,
double rotation_epsilon,
double translation_epsilon,
bool verbose) {
Registration<GICPFactor, ParallelReductionOMP> registration;
registration.rejector.max_dist_sq = max_correspondence_distance * max_correspondence_distance;
registration.reduction.num_threads = num_threads;
registration.optimizer.max_iterations = max_iterations;
registration.criteria.rotation_eps = rotation_epsilon;
registration.criteria.translation_eps = translation_epsilon;
registration.optimizer.verbose = verbose;

return registration.align(target_voxelmap, source, target_voxelmap, Eigen::Isometry3d(init_T_target_source));
Expand All @@ -234,6 +258,8 @@ void define_align(py::module& m) {
py::arg("max_correspondence_distance") = 1.0,
py::arg("num_threads") = 1,
py::arg("max_iterations") = 20,
py::arg("rotation_epsilon") = 0.1 * M_PI / 180.0,
py::arg("translation_epsilon") = 1e-3,
py::arg("verbose") = false,
R"pbdoc(
Align two point clouds using voxel-based GICP algorithm, utilizing a Gaussian Voxel Map.
Expand All @@ -254,6 +280,10 @@ void define_align(py::module& m) {
Number of threads to use for computation.
max_iterations : int = 20
Maximum number of iterations for the optimization algorithm.
rotation_epsilon: double = 0.1 * M_PI / 180.0
Convergence criteria for rotation change
translation_epsilon: double = 1e-3
Convergence criteria for transformation change
verbose : bool = False
If True, print debug information during the optimization process.

Expand Down
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