small_gicp (fast_gicp2)

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

• Highly optimized : The implementation of the core registration algorithm is further optimized from that in fast_gicp. It can provide up to 2x speed up compared to fast_gicp.
• All parallerized : small_gicp provides parallelized implementations of several algorithms in the point cloud registration process (Downsampling, KdTree construction, Normal/covariance estimation). As a parallelism backend, either (or both) of OpenMP and Intel TBB can be used.
• Minimum dependency : small_gicp is a header-only library and requires only Eigen (and bundled nanoflann and Sophus) at a minimum. Optionally, it provides the PCL registration interface so that it can be used as a drop-in replacement in many systems.
• Customizable : small_gicp is implemented with the trait mechanism that allows feeding any custom point cloud class to the registration algorithm. Furthermore, the template-based implementation allows customizing the regisration process with your custom correspondence estimator and registration factors.

Installation

This is a header-only library. You can just download and put it in your project directory to use all capabilities.

Meanwhile, if you just want to perform basic point cloud registration without fine customization, you can build and install the helper library (see small_gicp/registration/registration_helper.hpp for details) 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

Usage

The following examples assume using namespace small_gicp.

Using helper library (01_basic_resigtration.cpp)

The helper library (`registration_helper.hpp`) enables processing point clouds represented as `std::vector` easily.

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.

#include <small_gicp/registration/registration_helper.hpp>

std::vector<Eigen::Vector3d> target_points = ...;   // Any of Eigen::Vector(3|4)(f|d) can be used
std::vector<Eigen::Vector3d> 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<double, 6, 6> H = result.H;      // Final Hessian matrix (6x6)

There is also a way to perform preprocessing and registration separately. This enables saving the time for preprocessing in case registration is performed several times for a same point cloud (e.g., typical odometry estimation based on scan-to-scan matching).

#include <small_gicp/registration/registration_helper.hpp>

std::vector<Eigen::Vector3d> target_points = ...;   // Any of Eigen::Vector(3|4)(f|d) can be used
std::vector<Eigen::Vector3d> 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<PointCloud::Ptr, KdTree<PointCloud>::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<double, 6, 6> H = result.H;      // Final Hessian matrix (6x6)

Using with PCL interface (02_basic_resigtration_pcl.cpp)

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

#include <small_gicp/pcl/pcl_registration.hpp>

pcl::PointCloud<pcl::PointXYZ>::Ptr raw_target = ...;
pcl::PointCloud<pcl::PointXYZ>::Ptr raw_source = ...;

// small_gicp::voxelgrid_downsampling can directly operate on pcl::PointCloud.
pcl::PointCloud<pcl::PointXYZ>::Ptr target = voxelgrid_sampling_omp(*raw_target, 0.25);
pcl::PointCloud<pcl::PointXYZ>::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<pcl::PointXYZ, pcl::PointXYZ> 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<pcl::PointCloud<pcl::PointXYZ>>();
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.

#include <small_gicp/pcl/pcl_registration.hpp>

pcl::PointCloud<pcl::PointXYZ>::Ptr raw_target = ...;
pcl::PointCloud<pcl::PointXYZ>::Ptr raw_source = ...;

// Downsample points and convert them into pcl::PointCloud<pcl::PointCovariance>.
pcl::PointCloud<pcl::PointCovariance>::Ptr target = voxelgrid_sampling_omp<pcl::PointCloud<pcl::PointXYZ>, pcl::PointCloud<pcl::PointCovariance>>(*raw_target, 0.25);
pcl::PointCloud<pcl::PointCovariance>::Ptr source = voxelgrid_sampling_omp<pcl::PointCloud<pcl::PointXYZ>, pcl::PointCloud<pcl::PointCovariance>>(*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<KdTreeOMP<pcl::PointCloud<pcl::PointCovariance>>>(target, num_threads);
auto source_tree = std::make_shared<KdTreeOMP<pcl::PointCloud<pcl::PointCovariance>>>(source, num_threads);

Registration<GICPFactor, ParallelReductionOMP> 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<pcl::PointCovariance>.
auto result = registration.align(*target, *source, *target_tree, Eigen::Isometry3d::Identity());

Using small_gicp::Registration template (registration.hpp)

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

#include <small_gicp/ann/kdtree_omp.hpp>
#include <small_gicp/points/point_cloud.hpp>
#include <small_gicp/factors/gicp_factor.hpp>
#include <small_gicp/util/normal_estimation_omp.hpp>
#include <small_gicp/registration/reduction_omp.hpp>
#include <small_gicp/registration/registration.hpp>

std::vector<Eigen::Vector3d> target_points = ...;   // Any of Eigen::Vector(3|4)(f|d) can be used
std::vector<Eigen::Vector3d> 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<PointCloud>(target_points);
auto source = std::make_shared<PointCloud>(source_points);

// Downsampling
target = voxelgrid_downsampling_omp(*target, downsampling_resolution, num_threads);
source = voxelgrid_downsampling_omp(*source, downsampling_resolution, num_threads);

// Create KdTree
auto target_tree = std::make_shared<KdTreeOMP<PointCloud>>(target, num_threads);
auto source_tree = std::make_shared<KdTreeOMP<PointCloud>>(source, 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<GICPFactor, ParallelReductionOMP> 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<double, 6, 6> H = result.H;      // Final Hessian matrix (6x6)

Custom registration example:

using PerPointFactor = PointToPlaneICPFactor;       // Point-to-plane ICP
using GeneralFactor = RestrictDoFFactor;            // DoF restriction
using Reduction = ParallelReductionTBB;             // TBB-based parallel reduction
using CorrespondenceRejector = DistanceRejector;    // Distance-based correspondence rejection
using Optimizer = LevenbergMarquardtOptimizer;      // Levenberg marquardt optimizer

Registration<PerPointFactor, Reduction, GeneralFactor, CorrespondenceRejector, Optimizer> registration;
registration.general_factor.set_translation_mask(Eigen::Vector3d(1.0, 1.0, 0.0));   // XY-translation only
registration.general_factor.set_ratation_mask(Eigen::Vector3d(0.0, 0.0, 1.0));      // Z-rotation only
registration.optimizer.init_lambda = 1e-3;                                          // Initial damping scale

Benchmark

Downsampling

• Single-threaded small_gicp::voxelgrid_sampling is about 1.3x faster than pcl::VoxelGrid.
• Multi-threaded small_gicp::voxelgrid_sampling_tbb (6 threads) is about 3.2x faster than pcl::VoxelGrid.
• small_gicp::voxelgrid_sampling gives accurate downsampling results (almost identical to those of pcl::VoxelGrid) while pcl::ApproximateVoxelGrid yields spurious points (up to 2x points).
• small_gicp::voxelgrid_sampling can process a larger point cloud with a fine voxel resolution compared to pcl::VoxelGrid.

Odometry estimation

• Single-threaded small_gicp::GICP is about 2.4x and 1.9x faster than pcl::GICP and fast_gicp::GICP, respectively.
• small_gicp shows a better scalability to many-threads situations compared to fast_gicp.
• small_gicp::GICP with TBB flow graph shows an excellent multi-thread scalablity but with a latency degradation.

Papers

• Kenji Koide, Masashi Yokozuka, Shuji Oishi, and Atsuhiko Banno, Voxelized GICP for Fast and Accurate 3D Point Cloud Registration, ICRA2021

Contact

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