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koide3 / small_gicp

Efficient and parallel algorithms for point cloud registration [C++, Python]
MIT License
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cpp icp multi-threading open3d pcl point-cloud-regstration pointcloud python registration scan-matching

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small_gicp

small_gicp is a header-only C++ library providing efficient and parallelized algorithms for fine point cloud registration (ICP, Point-to-Plane ICP, GICP, VGICP, etc.). It is a refined and optimized version of its predecessor, fast_gicp, re-written from scratch with the following features.

Note that GPU-based implementations are NOT included in this package.

If you find this package useful for your project, please consider leaving a comment here. It would help the author receive recognition in his organization and keep working on this project.

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Requirements

This library uses C++17 features. The PCL interface is not compatible with PCL older than 1.11 that uses boost::shared_ptr.

Dependencies

Installation

C++

small_gicp is a header-only library. You can just download and drop it in your project directory to use it.

If you need only basic point cloud registration functions, you can build and install the helper library as follows.

sudo apt-get install libeigen3-dev libomp-dev

cd small_gicp
mkdir build && cd build
cmake .. -DCMAKE_BUILD_TYPE=Release && make -j
sudo make install

Python (Linux / Windows / MacOS)

Install from PyPI

pip install small_gicp

Install from source

cd small_gicp
pip install .

# [Optional (linux)] Install stubs for autocomplete (If you know a better way, let me know...)
pip install pybind11-stubgen
cd ~/.local/lib/python3.10/site-packages
pybind11-stubgen -o . --ignore-invalid=all small_gicp

Documentation

Usage (C++)

The following examples assume using namespace small_gicp is placed somewhere.

Using helper library (01_basic_registration.cpp)

The helper library (registration_helper.hpp) enables easily processing point clouds represented as std::vector<Eigen::Vector(3|4)(f|d)>.

Expand `small_gicp::align` takes two point clouds (`std::vectors` of `Eigen::Vector(3|4)(f|d)`) and returns a registration result (estimated transformation and some information on the optimization result). This is the easiest way to use small_gicp but causes an overhead for duplicated preprocessing. ```cpp #include std::vector target_points = ...; // Any of Eigen::Vector(3|4)(f|d) can be used std::vector source_points = ...; // RegistrationSetting setting; setting.num_threads = 4; // Number of threads to be used setting.downsampling_resolution = 0.25; // Downsampling resolution setting.max_correspondence_distance = 1.0; // Maximum correspondence distance between points (e.g., triming threshold) Eigen::Isometry3d init_T_target_source = Eigen::Isometry3d::Identity(); RegistrationResult result = align(target_points, source_points, init_T_target_source, setting); Eigen::Isometry3d T = result.T_target_source; // Estimated transformation size_t num_inliers = result.num_inliers; // Number of inlier source points Eigen::Matrix H = result.H; // Final Hessian matrix (6x6) ``` There is also a way to perform preprocessing and registration separately. This enables saving time for preprocessing in case registration is performed several times for the same point cloud (e.g., typical odometry estimation based on scan-to-scan matching). ```cpp #include std::vector target_points = ...; // Any of Eigen::Vector(3|4)(f|d) can be used std::vector source_points = ...; // int num_threads = 4; // Number of threads to be used double downsampling_resolution = 0.25; // Downsampling resolution int num_neighbors = 10; // Number of neighbor points used for normal and covariance estimation // std::pair::Ptr> auto [target, target_tree] = preprocess_points(target_points, downsampling_resolution, num_neighbors, num_threads); auto [source, source_tree] = preprocess_points(source_points, downsampling_resolution, num_neighbors, num_threads); RegistrationSetting setting; setting.num_threads = num_threads; setting.max_correspondence_distance = 1.0; // Maximum correspondence distance between points (e.g., triming threshold) Eigen::Isometry3d init_T_target_source = Eigen::Isometry3d::Identity(); RegistrationResult result = align(*target, *source, *target_tree, init_T_target_source, setting); Eigen::Isometry3d T = result.T_target_source; // Estimated transformation size_t num_inliers = result.num_inliers; // Number of inlier source points Eigen::Matrix H = result.H; // Final Hessian matrix (6x6) ```

Using PCL interface (02_basic_registration_pcl.cpp)

The PCL interface allows using small_gicp as a drop-in replacement for pcl::Registration. It is also possible to directly feed pcl::PointCloud to algorithms implemented in small_gicp.

Expand ```cpp #include pcl::PointCloud::Ptr raw_target = ...; pcl::PointCloud::Ptr raw_source = ...; // small_gicp::voxelgrid_downsampling can directly operate on pcl::PointCloud. pcl::PointCloud::Ptr target = voxelgrid_sampling_omp(*raw_target, 0.25); pcl::PointCloud::Ptr source = voxelgrid_sampling_omp(*raw_source, 0.25); // RegistrationPCL is derived from pcl::Registration and has mostly the same interface as pcl::GeneralizedIterativeClosestPoint. RegistrationPCL reg; reg.setNumThreads(4); reg.setCorrespondenceRandomness(20); reg.setMaxCorrespondenceDistance(1.0); reg.setVoxelResolution(1.0); reg.setRegistrationType("VGICP"); // or "GICP" (default = "GICP") // Set input point clouds. reg.setInputTarget(target); reg.setInputSource(source); // Align point clouds. auto aligned = pcl::make_shared>(); reg.align(*aligned); // Swap source and target and align again. // This is useful when you want to re-use preprocessed point clouds for successive registrations (e.g., odometry estimation). reg.swapSourceAndTarget(); reg.align(*aligned); ``` It is also possible to directly feed `pcl::PointCloud` to `small_gicp::Registration`. Because all preprocessed data are exposed in this way, you can easily re-use them to obtain the best efficiency. ```cpp #include #include pcl::PointCloud::Ptr raw_target = ...; pcl::PointCloud::Ptr raw_source = ...; // Downsample points and convert them into pcl::PointCloud. pcl::PointCloud::Ptr target = voxelgrid_sampling_omp, pcl::PointCloud>(*raw_target, 0.25); pcl::PointCloud::Ptr source = voxelgrid_sampling_omp, pcl::PointCloud>(*raw_source, 0.25); // Estimate covariances of points. const int num_threads = 4; const int num_neighbors = 20; estimate_covariances_omp(*target, num_neighbors, num_threads); estimate_covariances_omp(*source, num_neighbors, num_threads); // Create KdTree for target and source. auto target_tree = std::make_shared>>(target, KdTreeBuilderOMP(num_threads)); auto source_tree = std::make_shared>>(source, KdTreeBuilderOMP(num_threads)); Registration registration; registration.reduction.num_threads = num_threads; registration.rejector.max_dist_sq = 1.0; // Align point clouds. Note that the input point clouds are pcl::PointCloud. auto result = registration.align(*target, *source, *target_tree, Eigen::Isometry3d::Identity()); ```

Using Registration template (03_registration_template.cpp)

If you want to fine-control and customize the registration process, use small_gicp::Registration template that allows modifying the inner algorithms and parameters.

Expand ```cpp #include #include #include #include #include #include std::vector target_points = ...; // Any of Eigen::Vector(3|4)(f|d) can be used std::vector source_points = ...; // int num_threads = 4; double downsampling_resolution = 0.25; int num_neighbors = 10; double max_correspondence_distance = 1.0; // Convert to small_gicp::PointCloud auto target = std::make_shared(target_points); auto source = std::make_shared(source_points); // Downsampling target = voxelgrid_sampling_omp(*target, downsampling_resolution, num_threads); source = voxelgrid_sampling_omp(*source, downsampling_resolution, num_threads); // Create KdTree auto target_tree = std::make_shared>(target, KdTreeBuilderOMP(num_threads)); auto source_tree = std::make_shared>(source, KdTreeBuilderOMP(num_threads)); // Estimate point covariances estimate_covariances_omp(*target, *target_tree, num_neighbors, num_threads); estimate_covariances_omp(*source, *source_tree, num_neighbors, num_threads); // GICP + OMP-based parallel reduction Registration registration; registration.reduction.num_threads = num_threads; registration.rejector.max_dist_sq = max_correspondence_distance * max_correspondence_distance; // Align point clouds Eigen::Isometry3d init_T_target_source = Eigen::Isometry3d::Identity(); auto result = registration.align(*target, *source, *target_tree, init_T_target_source); Eigen::Isometry3d T = result.T_target_source; // Estimated transformation size_t num_inliers = result.num_inliers; // Number of inlier source points Eigen::Matrix H = result.H; // Final Hessian matrix (6x6) ``` See [03_registration_template.cpp](src/example/03_registration_template.cpp) for more detailed customization examples.

Cookbook

Usage (Python) basic_registration.py

Expand Example A : Perform registration with numpy arrays ```python # Align two point clouds using various ICP-like algorithms. # # Parameters # ---------- # target_points : NDArray[np.float64] # Nx3 or Nx4 matrix representing the target point cloud. # source_points : NDArray[np.float64] # Nx3 or Nx4 matrix representing the source point cloud. # init_T_target_source : np.ndarray[np.float64] # 4x4 matrix representing the initial transformation from target to source. # registration_type : str = 'GICP' # Type of registration algorithm to use ('ICP', 'PLANE_ICP', 'GICP', 'VGICP'). # voxel_resolution : float = 1.0 # Resolution of voxels used for correspondence search (used only in VGICP). # downsampling_resolution : float = 0.25 # Resolution for downsampling the point clouds. # max_correspondence_distance : float = 1.0 # Maximum distance for matching points between point clouds. # num_threads : int = 1 # Number of threads to use for parallel processing. # # Returns # ------- # RegistrationResult # Object containing the final transformation matrix and convergence status. result = small_gicp.align(target_raw_numpy, source_raw_numpy, downsampling_resolution=0.25) result.T_target_source # Estimated transformation (4x4 numpy array) result.converged # If true, the optimization converged successfully result.iterations # Number of iterations the optimization took result.num_inliers # Number of inlier points result.H # Final Hessian matrix (6x6 matrix) result.b # Final information vector (6D vector) result.e # Final error (float) ``` Example B : Perform preprocessing and registration separately ```python # Preprocess point cloud (downsampling, kdtree construction, and normal/covariance estimation) # # Parameters # ---------- # points : NDArray[np.float64] # Nx3 or Nx4 matrix representing the point cloud. # downsampling_resolution : float = 0.1 # Resolution for downsampling the point clouds. # num_neighbors : int = 20 # Number of neighbor points to usefor point normal/covariance estimation. # num_threads : int = 1 # Number of threads to use for parallel processing. # # Returns # ------- # PointCloud # Downsampled point cloud with estimated normals and covariances. # KdTree # KdTree for the downsampled point cloud target, target_tree = small_gicp.preprocess_points(target_raw_numpy, downsampling_resolution=0.25) source, source_tree = small_gicp.preprocess_points(source_raw_numpy, downsampling_resolution=0.25) # `target` and `source` are small_gicp.PointCloud with the following methods target.size() # Number of points target.points() # Nx4 numpy array [x, y, z, 1] x N target.normals() # Nx4 numpy array [nx, ny, nz, 0] x N target.covs() # Array of 4x4 covariance matrices # Align two point clouds using specified ICP-like algorithms, utilizing point cloud and KD-tree inputs. # # Parameters # ---------- # target : PointCloud::ConstPtr # Pointer to the target point cloud. # source : PointCloud::ConstPtr # Pointer to the source point cloud. # target_tree : KdTree::ConstPtr, optional # Pointer to the KD-tree of the target for nearest neighbor search. If nullptr, a new tree is built. # init_T_target_source : NDArray[np.float64] # 4x4 matrix representing the initial transformation from target to source. # registration_type : str = 'GICP' # Type of registration algorithm to use ('ICP', 'PLANE_ICP', 'GICP'). # max_correspondence_distance : float = 1.0 # Maximum distance for corresponding point pairs. # num_threads : int = 1 # Number of threads to use for computation. # # Returns # ------- # RegistrationResult # Object containing the final transformation matrix and convergence status. result = small_gicp.align(target, source, target_tree) ``` Example C : Perform each of preprocessing steps one-by-one ```python # Convert numpy arrays (Nx3 or Nx4) to small_gicp.PointCloud target_raw = small_gicp.PointCloud(target_raw_numpy) source_raw = small_gicp.PointCloud(source_raw_numpy) # Downsampling target = small_gicp.voxelgrid_sampling(target_raw, 0.25) source = small_gicp.voxelgrid_sampling(source_raw, 0.25) # KdTree construction target_tree = small_gicp.KdTree(target) source_tree = small_gicp.KdTree(source) # Estimate covariances small_gicp.estimate_covariances(target, target_tree) small_gicp.estimate_covariances(source, source_tree) # Align point clouds result = small_gicp.align(target, source, target_tree) ``` Example D: Example with Open3D ```python target_o3d = open3d.io.read_point_cloud('small_gicp/data/target.ply').paint_uniform_color([0, 1, 0]) source_o3d = open3d.io.read_point_cloud('small_gicp/data/source.ply').paint_uniform_color([0, 0, 1]) target, target_tree = small_gicp.preprocess_points(numpy.asarray(target_o3d.points), downsampling_resolution=0.25) source, source_tree = small_gicp.preprocess_points(numpy.asarray(source_o3d.points), downsampling_resolution=0.25) result = small_gicp.align(target, source, target_tree) source_o3d.transform(result.T_target_source) open3d.visualization.draw_geometries([target_o3d, source_o3d]) ```

Cookbook

Running examples

C++

cd small_gicp
mkdir build && cd build
cmake .. -DBUILD_EXAMPLES=ON && make -j

cd ..
./build/01_basic_registration
./build/02_basic_registration_pcl
./build/03_registration_template

Python

cd small_gicp
pip install .

python3 src/example/basic_registration.py

Benchmark

Processing speed comparison between small_gicp and Open3D (youtube). small_comp

Downsampling

downsampling_comp

KdTree construction

kdtree_time

Odometry estimation

odometry_time

License

This package is released under the MIT license.

If you find this package useful for your project, please consider leaving a comment here. It would help the author receive recognition in his organization and keep working on this project.

Contact

Kenji Koide, National Institute of Advanced Industrial Science and Technology (AIST)