From 5e983ac3b2b3d199db41713d7e37d1063842d8fe Mon Sep 17 00:00:00 2001 From: "k.koide" Date: Fri, 29 Mar 2024 21:18:45 +0900 Subject: [PATCH] examples --- CMakeLists.txt | 34 ++- README.md | 245 ++++++++++++++++++ docs/assets/downsampling_comp.png | Bin 0 -> 217132 bytes docs/assets/downsampling_threads.png | Bin 0 -> 94855 bytes docs/assets/odometry_time.png | Bin 0 -> 452009 bytes .../small_gicp/benchmark/benchmark_odom.hpp | 4 +- include/small_gicp/pcl/pcl_proxy.hpp | 35 +++ include/small_gicp/pcl/pcl_registration.hpp | 84 ++++++ .../small_gicp/pcl/pcl_registration_impl.hpp | 140 ++++++++++ .../registration/registration_helper.hpp | 8 +- scripts/plot_downsampling.py | 4 + scripts/plot_odometry.py | 1 + src/example/01_basic_registration.cpp | 68 +++++ src/example/02_basic_registration_pcl.cpp | 107 ++++++++ src/odometry_benchmark.cpp | 4 +- ...odometry_benchmark_small_gicp_tbb_flow.cpp | 2 +- .../registration/registration_helper.cpp | 14 +- 17 files changed, 733 insertions(+), 17 deletions(-) create mode 100644 README.md create mode 100644 docs/assets/downsampling_comp.png create mode 100644 docs/assets/downsampling_threads.png create mode 100644 docs/assets/odometry_time.png create mode 100644 include/small_gicp/pcl/pcl_proxy.hpp create mode 100644 include/small_gicp/pcl/pcl_registration.hpp create mode 100644 include/small_gicp/pcl/pcl_registration_impl.hpp create mode 100644 src/example/01_basic_registration.cpp create mode 100644 src/example/02_basic_registration_pcl.cpp diff --git a/CMakeLists.txt b/CMakeLists.txt index 54345b1..68a7b26 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -16,7 +16,8 @@ set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OpenMP_C_FLAGS}") set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}") option(BUILD_HELPER "Build helper library" ON) -option(BUILD_TESTS "Build test" OFF) +option(BUILD_TESTS "Build tests" OFF) +option(BUILD_EXAMPLES "Build examples" OFF) option(BUILD_BENCHMARKS "Build benchmarks" OFF) option(BUILD_WITH_TBB "Build with TBB" ON) option(BUILD_WITH_PCL "Build with PCL (required for benchmark and test only)" OFF) @@ -124,6 +125,37 @@ if(BUILD_BENCHMARKS) endif() endif() +############# +## Example ## +############# +if(BUILD_EXAMPLES) +find_package(fmt REQUIRED) +find_package(PCL REQUIRED) +find_package(TBB REQUIRED) +find_package(Iridescence REQUIRED) + +file(GLOB EXAMPLE_SOURCES "src/example/*.cpp") +foreach(EXAMPLE_SOURCE ${EXAMPLE_SOURCES}) + get_filename_component(EXAMPLE_NAME ${EXAMPLE_SOURCE} NAME_WE) + add_executable(${EXAMPLE_NAME} ${EXAMPLE_SOURCE}) + target_include_directories(${EXAMPLE_NAME} PUBLIC + include + ${PCL_INCLUDE_DIRS} + ${TBB_INCLUDE_DIRS} + ${EIGEN3_INCLUDE_DIR} + ${Iridescence_INCLUDE_DIRS} + ) + target_link_libraries(${EXAMPLE_NAME} + small_gicp + fmt::fmt + ${PCL_LIBRARIES} + ${TBB_LIBRARIES} + ${Iridescence_LIBRARIES} + ) +endforeach() + + +endif() ########## ## Test ## diff --git a/README.md b/README.md new file mode 100644 index 0000000..a5c2840 --- /dev/null +++ b/README.md @@ -0,0 +1,245 @@ +# 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](https://github.com/SMRT-AIST/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](https://www.openmp.org/) and [Intel TBB](https://github.com/oneapi-src/oneTBB) can be used. +- **Minimum dependency** : small_gicp is a header-only library and requires only [Eigen](https://eigen.tuxfamily.org/) (and bundled [nanoflann](https://github.com/jlblancoc/nanoflann) and [Sophus](https://github.com/strasdat/Sophus)) at a minimum. Optionally, it provides the [PCL](https://pointclouds.org/) 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. + +[![Build](https://github.com/koide3/small_gicp/actions/workflows/build.yml/badge.svg)](https://github.com/koide3/small_gicp/actions/workflows/build.yml) + +## 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. + +```cpp +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](https://github.com/koide3/small_gicp/blob/master/src/example/01_basic_registration.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. + +```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 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). + +```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 with PCL interface ([02_basic_resigtration_pcl.cpp](https://github.com/koide3/small_gicp/blob/master/src/example/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`. + +```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 + +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, num_threads); +auto source_tree = std::make_shared>>(source, 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 `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. + +```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_downsampling_omp(*target, downsampling_resolution, num_threads); +source = voxelgrid_downsampling_omp(*source, downsampling_resolution, num_threads); + +// Create KdTree +auto target_tree = std::make_shared>(target, num_threads); +auto source_tree = std::make_shared>(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 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) +``` + +Custom registration example: + +```cpp +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 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`. + +![downsampling_comp](docs/assets/downsampling_comp.png) + +### 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. + +![odometry_time](docs/assets/odometry_time.png) + +## Papers +- Kenji Koide, Masashi Yokozuka, Shuji Oishi, and Atsuhiko Banno, Voxelized GICP for Fast and Accurate 3D Point Cloud Registration, ICRA2021 + +## Contact + +[Kenji Koide](https://staff.aist.go.jp/k.koide/), National Institute of Advanced Industrial Science and Technology (AIST) \ No newline at end of file diff --git a/docs/assets/downsampling_comp.png b/docs/assets/downsampling_comp.png new file mode 100644 index 0000000000000000000000000000000000000000..e3948d9363d0bf597c54544b28132f2799632285 GIT binary patch literal 217132 zcmZs@1yq&W_XTQ7@yUUX`w#`}Th zXf`a_f&cja^A(3#0{xL{_`R#X-u46pk6b?k|vYdm^+JNIA>SKXMM{h#Q#5+*_$jB z96h#$2vlv|AVY36#hVFiL~Sg4`;{>twEjOoEuPl096-xqNxyaV2lf_b0sWsj2F%byO8h#r&8g&0p9j)Be2tcYRF@!sAqRbb9Qe z!3M44RDZurnuRdo@Q8|kc2Pr|k&$(CM5D(h`9Vt6dJNs30LB_-E&>CUse>YkFvN%V zfzssTzqjki<jr5fd-dVdPhtYPgaV|6?9laRN@usCa zD*s8^jhlDa+f?}P18GXp(dxMGC22njIXOC!D*4;m+O~CcG>nZ==<4b^1<)41A26e1 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a/include/small_gicp/benchmark/benchmark_odom.hpp b/include/small_gicp/benchmark/benchmark_odom.hpp index 251e3d0..ab7fddc 100644 --- a/include/small_gicp/benchmark/benchmark_odom.hpp +++ b/include/small_gicp/benchmark/benchmark_odom.hpp @@ -19,7 +19,7 @@ struct OdometryEstimationParams { bool visualize = false; int num_threads = 4; int num_neighbors = 20; - double downsample_resolution = 0.25; + double downsampling_resolution = 0.25; double voxel_resolution = 1.0; double max_correspondence_distance = 1.0; }; @@ -54,7 +54,7 @@ class OnlineOdometryEstimation : public OdometryEstimation { report(); } - auto downsampled = voxelgrid_sampling(*points[i], params.downsample_resolution); + auto downsampled = voxelgrid_sampling(*points[i], params.downsampling_resolution); const Eigen::Isometry3d T = estimate(downsampled); traj.emplace_back(T); diff --git a/include/small_gicp/pcl/pcl_proxy.hpp b/include/small_gicp/pcl/pcl_proxy.hpp new file mode 100644 index 0000000..6ab7b3a --- /dev/null +++ b/include/small_gicp/pcl/pcl_proxy.hpp @@ -0,0 +1,35 @@ +#pragma once + +#include +#include + +namespace small_gicp { + +template +struct PointCloudProxy { + PointCloudProxy(const pcl::PointCloud& cloud, std::vector& covs) : cloud(cloud), covs(covs) {} + + const pcl::PointCloud& cloud; + std::vector& covs; +}; + +namespace traits { +template +struct Traits> { + using Points = PointCloudProxy; + + static size_t size(const Points& points) { return points.cloud.size(); } + + static bool has_points(const Points& points) { return !points.cloud.points.empty(); } + static bool has_covs(const Points& points) { return !points.covs.empty(); } + + static const Eigen::Vector4d point(const Points& points, size_t i) { return points.cloud.at(i).getVector4fMap().template cast(); } + static const Eigen::Matrix4d& cov(const Points& points, size_t i) { return points.covs[i]; } + + static void resize(Points& points, size_t n) { points.covs.resize(n); } + static void set_cov(Points& points, size_t i, const Eigen::Matrix4d& cov) { points.covs[i] = cov; } +}; + +} // namespace traits + +} // namespace small_gicp diff --git a/include/small_gicp/pcl/pcl_registration.hpp b/include/small_gicp/pcl/pcl_registration.hpp new file mode 100644 index 0000000..23da278 --- /dev/null +++ b/include/small_gicp/pcl/pcl_registration.hpp @@ -0,0 +1,84 @@ +#pragma once + +#include +#include +#include +#include + +namespace small_gicp { + +template +class RegistrationPCL : public pcl::Registration { +public: + using Scalar = float; + using Matrix4 = typename pcl::Registration::Matrix4; + + using PointCloudSource = typename pcl::Registration::PointCloudSource; + using PointCloudSourcePtr = typename PointCloudSource::Ptr; + using PointCloudSourceConstPtr = typename PointCloudSource::ConstPtr; + + using PointCloudTarget = typename pcl::Registration::PointCloudTarget; + using PointCloudTargetPtr = typename PointCloudTarget::Ptr; + using PointCloudTargetConstPtr = typename PointCloudTarget::ConstPtr; + + using Ptr = pcl::shared_ptr>; + using ConstPtr = pcl::shared_ptr>; + +protected: + using pcl::Registration::reg_name_; + using pcl::Registration::input_; + using pcl::Registration::target_; + using pcl::Registration::corr_dist_threshold_; + + using pcl::Registration::nr_iterations_; + using pcl::Registration::max_iterations_; + using pcl::Registration::final_transformation_; + + using pcl::Registration::transformation_epsilon_; + using pcl::Registration::converged_; + +public: + RegistrationPCL(); + virtual ~RegistrationPCL(); + + void setNumThreads(int n) { num_threads_ = n; } + void setCorrespondenceRandomness(int k) { k_correspondences_ = k; } + void setVoxelResolution(double r) { voxel_resolution_ = r; } + void setRotationEpsilon(double eps) { rotation_epsilon_ = eps; } + void setRegistrationType(const std::string& type); + + const Eigen::Matrix& getFinalHessian() const { return final_hessian_; } + + void setInputSource(const PointCloudSourceConstPtr& cloud) override; + void setInputTarget(const PointCloudTargetConstPtr& cloud) override; + + void swapSourceAndTarget(); + void clearSource(); + void clearTarget(); + +protected: + virtual void computeTransformation(PointCloudSource& output, const Matrix4& guess) override; + +protected: + int num_threads_; + int k_correspondences_; + double rotation_epsilon_; + double voxel_resolution_; + bool verbose_; + std::string registration_type_; + + std::shared_ptr>> target_tree_; + std::shared_ptr>> source_tree_; + + std::shared_ptr target_voxelmap_; + std::shared_ptr source_voxelmap_; + + std::vector target_covs_; + std::vector source_covs_; + + Eigen::Matrix final_hessian_; +}; + +} // namespace small_gicp + +#include \ No newline at end of file diff --git a/include/small_gicp/pcl/pcl_registration_impl.hpp b/include/small_gicp/pcl/pcl_registration_impl.hpp new file mode 100644 index 0000000..94ced9b --- /dev/null +++ b/include/small_gicp/pcl/pcl_registration_impl.hpp @@ -0,0 +1,140 @@ +#pragma once + +#include + +#include +#include +#include + +#include +#include +#include + +namespace small_gicp { + +template +RegistrationPCL::RegistrationPCL() { + reg_name_ = "RegistrationPCL"; + + num_threads_ = 4; + k_correspondences_ = 20; + corr_dist_threshold_ = 1000.0; + rotation_epsilon_ = 2e-3; + transformation_epsilon_ = 5e-4; + + voxel_resolution_ = 1.0; + verbose_ = false; + registration_type_ = "GICP"; + + final_hessian_.setIdentity(); +} + +template +RegistrationPCL::~RegistrationPCL() {} + +template +void RegistrationPCL::setInputSource(const PointCloudSourceConstPtr& cloud) { + if (input_ == cloud) { + return; + } + + pcl::Registration::setInputSource(cloud); + source_tree_ = std::make_shared>>(input_, num_threads_); + source_covs_.clear(); + source_voxelmap_.reset(); +} + +template +void RegistrationPCL::setInputTarget(const PointCloudTargetConstPtr& cloud) { + if (target_ == cloud) { + return; + } + + pcl::Registration::setInputTarget(cloud); + target_tree_ = std::make_shared>>(target_, num_threads_); + target_covs_.clear(); + target_voxelmap_.reset(); +} + +template +void RegistrationPCL::swapSourceAndTarget() { + input_.swap(target_); + source_tree_.swap(target_tree_); + source_covs_.swap(target_covs_); + source_voxelmap_.swap(target_voxelmap_); +} + +template +void RegistrationPCL::clearSource() { + input_.reset(); + source_tree_.reset(); + source_covs_.clear(); + source_voxelmap_.reset(); +} + +template +void RegistrationPCL::clearTarget() { + target_.reset(); + target_tree_.reset(); + target_covs_.clear(); + target_voxelmap_.reset(); +} + +template +void RegistrationPCL::setRegistrationType(const std::string& type) { + if (type == "GICP") { + registration_type_ = type; + } else if (type == "VGICP") { + registration_type_ = type; + } else { + PCL_ERROR("[RegistrationPCL::setRegistrationType] Invalid registration type: %s\n", type.c_str()); + } +} + +template +void RegistrationPCL::computeTransformation(PointCloudSource& output, const Matrix4& guess) { + if (output.points.data() == input_->points.data() || output.points.data() == target_->points.data()) { + throw std::invalid_argument("FastGICP: destination cloud cannot be identical to source or target"); + } + + PointCloudProxy source_proxy(*input_, source_covs_); + PointCloudProxy target_proxy(*target_, target_covs_); + + if (source_covs_.size() != input_->size()) { + estimate_covariances_omp(source_proxy, *source_tree_, k_correspondences_, num_threads_); + } + if (target_covs_.size() != target_->size()) { + estimate_covariances_omp(target_proxy, *target_tree_, k_correspondences_, num_threads_); + } + + Registration registration; + registration.reduction.num_threads = num_threads_; + registration.rejector.max_dist_sq = corr_dist_threshold_ * corr_dist_threshold_; + registration.optimizer.verbose = verbose_; + registration.optimizer.max_iterations = max_iterations_; + + RegistrationResult result(Eigen::Isometry3d::Identity()); + if (registration_type_ == "GICP") { + result = registration.align(target_proxy, source_proxy, *target_tree_, Eigen::Isometry3d(guess.template cast())); + } else if (registration_type_ == "VGICP") { + if (!target_voxelmap_) { + target_voxelmap_ = std::make_shared(voxel_resolution_); + target_voxelmap_->insert(target_proxy); + } + if (!source_voxelmap_) { + source_voxelmap_ = std::make_shared(voxel_resolution_); + source_voxelmap_->insert(source_proxy); + } + + result = registration.align(*target_voxelmap_, source_proxy, *target_voxelmap_, Eigen::Isometry3d(guess.template cast())); + } else { + PCL_ERROR("[RegistrationPCL::computeTransformation] Invalid registration type: %s\n", registration_type_.c_str()); + return; + } + + final_transformation_ = result.T_target_source.matrix().template cast(); + final_hessian_ = result.H; + pcl::transformPointCloud(*input_, output, final_transformation_); +} + +} // namespace small_gicp diff --git a/include/small_gicp/registration/registration_helper.hpp b/include/small_gicp/registration/registration_helper.hpp index 55c9bfc..7409e85 100644 --- a/include/small_gicp/registration/registration_helper.hpp +++ b/include/small_gicp/registration/registration_helper.hpp @@ -9,17 +9,17 @@ namespace small_gicp { /// @brief Preprocess point cloud (downsampling, kdtree creation, and normal and covariance estimation) /// @param points Input points -/// @param downsample_resolution Downsample resolution +/// @param downsampling_resolution Downsample resolution /// @param num_neighbors Number of neighbors for normal/covariance estimation /// @param num_threads Number of threads std::pair>> -preprocess_points(const PointCloud& points, double downsample_resolution, int num_neighbors = 10, int num_threads = 4); +preprocess_points(const PointCloud& points, double downsampling_resolution, int num_neighbors = 10, int num_threads = 4); /// @brief Preprocess point cloud (downsampling, kdtree creation, and normal and covariance estimation) /// @note This function only accepts Eigen::Vector(3|4)(f|d) as input template std::pair>> -preprocess_points(const std::vector>& points, double downsample_resolution, int num_neighbors = 10, int num_threads = 4); +preprocess_points(const std::vector>& points, double downsampling_resolution, int num_neighbors = 10, int num_threads = 4); /// @brief Create Gaussian voxel map /// @param points Input points @@ -32,7 +32,7 @@ struct RegistrationSetting { RegistrationType type = GICP; ///< Registration type double voxel_resolution = 1.0; ///< Voxel resolution for VGICP - double downsample_resolution = 0.25; ///< Downsample resolution (this will be used only in the Eigen-based interface) + double downsampling_resolution = 0.25; ///< Downsample resolution (this will be used only in the Eigen-based interface) double max_correspondence_distance = 1.0; ///< Maximum correspondence distance int num_threads = 4; ///< Number of threads }; diff --git a/scripts/plot_downsampling.py b/scripts/plot_downsampling.py index 8fa4a17..6b027bb 100644 --- a/scripts/plot_downsampling.py +++ b/scripts/plot_downsampling.py @@ -94,6 +94,8 @@ def summarize(rets): axes[0].set_ylabel('Processing time [msec/scan]') axes[2].legend(loc='upper center', bbox_to_anchor=(0.5, -0.11), ncol=3) + fig.savefig('downsampling_threads.svg') + fig, axes = pyplot.subplots(1, 2) fig.set_size_inches(18, 3) @@ -116,6 +118,8 @@ def summarize(rets): axes[0].grid() axes[1].grid() + fig.savefig('downsampling_comp.svg') + pyplot.show() diff --git a/scripts/plot_odometry.py b/scripts/plot_odometry.py index 492064f..5beaea2 100644 --- a/scripts/plot_odometry.py +++ b/scripts/plot_odometry.py @@ -82,6 +82,7 @@ def main(): axes[i, j].legend() axes[i, j].grid() + fig.savefig('odometry_time.svg') pyplot.show() if __name__ == "__main__": diff --git a/src/example/01_basic_registration.cpp b/src/example/01_basic_registration.cpp new file mode 100644 index 0000000..970bfa2 --- /dev/null +++ b/src/example/01_basic_registration.cpp @@ -0,0 +1,68 @@ +/// @brief Basic point cloud registration example with small_gicp::align() +#include +#include +#include + +using namespace small_gicp; + +/// @brief Most basic registration example. +void example1(const std::vector& target_points, const 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); + + std::cout << "--- T_target_source ---" << std::endl << result.T_target_source.matrix() << std::endl; + std::cout << "converged:" << result.converged << std::endl; + std::cout << "error:" << result.error << std::endl; + std::cout << "iterations:" << result.iterations << std::endl; + std::cout << "num_inliers:" << result.num_inliers << std::endl; + std::cout << "--- H ---" << std::endl << result.H << std::endl; + std::cout << "--- b ---" << std::endl << result.b.transpose() << std::endl; +} + +/// @brief Example to perform preprocessing and registration separately. +void example2(const std::vector& target_points, const 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); + + std::cout << "--- T_target_source ---" << std::endl << result.T_target_source.matrix() << std::endl; + std::cout << "converged:" << result.converged << std::endl; + std::cout << "error:" << result.error << std::endl; + std::cout << "iterations:" << result.iterations << std::endl; + std::cout << "num_inliers:" << result.num_inliers << std::endl; + std::cout << "--- H ---" << std::endl << result.H << std::endl; + std::cout << "--- b ---" << std::endl << result.b.transpose() << std::endl; + + // Preprocessed points and trees can be reused for the next registration for efficiency + RegistrationResult result2 = align(*source, *target, *source_tree, Eigen::Isometry3d::Identity(), setting); +} + +int main(int argc, char** argv) { + std::vector target_points = read_ply("data/target.ply"); + std::vector source_points = read_ply("data/source.ply"); + if (target_points.empty() || source_points.empty()) { + std::cerr << "error: failed to read points from data/(target|source).ply" << std::endl; + return 1; + } + + example1(target_points, source_points); + example2(target_points, source_points); + + return 0; +} \ No newline at end of file diff --git a/src/example/02_basic_registration_pcl.cpp b/src/example/02_basic_registration_pcl.cpp new file mode 100644 index 0000000..ef18604 --- /dev/null +++ b/src/example/02_basic_registration_pcl.cpp @@ -0,0 +1,107 @@ +/// @brief Basic point cloud registration example with PCL interfaces +#include +#include +#include +#include +#include + +#include +#include + +using namespace small_gicp; + +/// @brief Example of using RegistrationPCL that can be used as a drop-in replacement for pcl::GeneralizedIterativeClosestPoint. +void example1(const pcl::PointCloud::ConstPtr& raw_target, const pcl::PointCloud::ConstPtr& 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); + + std::cout << "--- T_target_source ---" << std::endl << reg.getFinalTransformation() << std::endl; + std::cout << "--- H ---" << std::endl << reg.getFinalHessian() << std::endl; + + // 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); + + std::cout << "--- T_target_source ---" << std::endl << reg.getFinalTransformation().inverse() << std::endl; +} + +/// @brief Example to directly feed pcl::PointCloud to small_gicp::Registration. +void example2(const pcl::PointCloud::ConstPtr& raw_target, const pcl::PointCloud::ConstPtr& 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, num_threads); + auto source_tree = std::make_shared>>(source, 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()); + + std::cout << "--- T_target_source ---" << std::endl << result.T_target_source.matrix() << std::endl; + std::cout << "converged:" << result.converged << std::endl; + std::cout << "error:" << result.error << std::endl; + std::cout << "iterations:" << result.iterations << std::endl; + std::cout << "num_inliers:" << result.num_inliers << std::endl; + std::cout << "--- H ---" << std::endl << result.H << std::endl; + std::cout << "--- b ---" << std::endl << result.b.transpose() << std::endl; + + // Because this usage exposes all preprocessed data, you can easily re-use them to obtain the best efficiency. + auto result2 = registration.align(*source, *target, *source_tree, Eigen::Isometry3d::Identity()); + + std::cout << "--- T_target_source ---" << std::endl << result2.T_target_source.inverse().matrix() << std::endl; +} + +int main(int argc, char** argv) { + std::vector target_points = read_ply("data/target.ply"); + std::vector source_points = read_ply("data/source.ply"); + if (target_points.empty() || source_points.empty()) { + std::cerr << "error: failed to read points from data/(target|source).ply" << std::endl; + return 1; + } + + const auto convert_to_pcl = [](const std::vector& raw_points) { + auto points = pcl::make_shared>(); + points->resize(raw_points.size()); + for (size_t i = 0; i < raw_points.size(); i++) { + points->at(i).getVector4fMap() = raw_points[i]; + } + return points; + }; + + pcl::PointCloud::Ptr raw_target = convert_to_pcl(target_points); + pcl::PointCloud::Ptr raw_source = convert_to_pcl(source_points); + + example1(raw_target, raw_source); + example2(raw_target, raw_source); + + return 0; +} \ No newline at end of file diff --git a/src/odometry_benchmark.cpp b/src/odometry_benchmark.cpp index 04a70e8..c9b2799 100644 --- a/src/odometry_benchmark.cpp +++ b/src/odometry_benchmark.cpp @@ -43,7 +43,7 @@ int main(int argc, char** argv) { } else if (arg == "--num_neighbors") { params.num_neighbors = std::stoi(argv[i + 1]); } else if (arg == "--downsampling_resolution") { - params.downsample_resolution = std::stod(argv[i + 1]); + params.downsampling_resolution = std::stod(argv[i + 1]); } else if (arg == "--voxel_resolution") { params.voxel_resolution = std::stod(argv[i + 1]); } else if (arg == "--engine") { @@ -59,7 +59,7 @@ int main(int argc, char** argv) { std::cout << "registration_engine=" << engine << std::endl; std::cout << "num_threads=" << params.num_threads << std::endl; std::cout << "num_neighbors=" << params.num_neighbors << std::endl; - std::cout << "downsampling_resolution=" << params.downsample_resolution << std::endl; + std::cout << "downsampling_resolution=" << params.downsampling_resolution << std::endl; std::cout << "voxel_resolution=" << params.voxel_resolution << std::endl; std::cout << "visualize=" << params.visualize << std::endl; diff --git a/src/odometry_benchmark_small_gicp_tbb_flow.cpp b/src/odometry_benchmark_small_gicp_tbb_flow.cpp index 4378dad..8348fcc 100644 --- a/src/odometry_benchmark_small_gicp_tbb_flow.cpp +++ b/src/odometry_benchmark_small_gicp_tbb_flow.cpp @@ -46,7 +46,7 @@ class SmallGICPFlowEstimationTBB : public OdometryEstimation { tbb::flow::broadcast_node input_node(graph); tbb::flow::function_node preprocess_node(graph, tbb::flow::unlimited, [&](const InputFrame::Ptr& input) { input->sw.start(); - input->points = voxelgrid_sampling(*input->points, params.downsample_resolution); + input->points = voxelgrid_sampling(*input->points, params.downsampling_resolution); input->kdtree = std::make_shared>(input->points); estimate_covariances(*input->points, *input->kdtree, params.num_neighbors); return input; diff --git a/src/small_gicp/registration/registration_helper.cpp b/src/small_gicp/registration/registration_helper.cpp index 271c521..3ecb651 100644 --- a/src/small_gicp/registration/registration_helper.cpp +++ b/src/small_gicp/registration/registration_helper.cpp @@ -17,14 +17,14 @@ namespace small_gicp { // Preprocess points -std::pair>> preprocess_points(const PointCloud& points, double downsample_resolution, int num_neighbors, int num_threads) { +std::pair>> preprocess_points(const PointCloud& points, double downsampling_resolution, int num_neighbors, int num_threads) { if (num_threads == 1) { - auto downsampled = voxelgrid_sampling(points, downsample_resolution); + auto downsampled = voxelgrid_sampling(points, downsampling_resolution); auto kdtree = std::make_shared>(downsampled); estimate_normals_covariances(*downsampled, *kdtree, num_neighbors); return {downsampled, kdtree}; } else { - auto downsampled = voxelgrid_sampling_omp(points, downsample_resolution); + auto downsampled = voxelgrid_sampling_omp(points, downsampling_resolution); auto kdtree = std::make_shared>(downsampled); estimate_normals_covariances_omp(*downsampled, *kdtree, num_neighbors, num_threads); return {downsampled, kdtree}; @@ -34,8 +34,8 @@ std::pair>> preprocess_point // Preprocess points with Eigen input template std::pair>> -preprocess_points(const std::vector>& points, double downsample_resolution, int num_neighbors, int num_threads) { - return preprocess_points(*std::make_shared(points), downsample_resolution, num_neighbors, num_threads); +preprocess_points(const std::vector>& points, double downsampling_resolution, int num_neighbors, int num_threads) { + return preprocess_points(*std::make_shared(points), downsampling_resolution, num_neighbors, num_threads); } // Explicit instantiation @@ -55,8 +55,8 @@ GaussianVoxelMap::Ptr create_gaussian_voxelmap(const PointCloud& points, double template RegistrationResult align(const std::vector>& target, const std::vector>& source, const Eigen::Isometry3d& init_T, const RegistrationSetting& setting) { - auto [target_points, target_tree] = preprocess_points(*std::make_shared(target), setting.downsample_resolution, 10, setting.num_threads); - auto [source_points, source_tree] = preprocess_points(*std::make_shared(source), setting.downsample_resolution, 10, setting.num_threads); + auto [target_points, target_tree] = preprocess_points(*std::make_shared(target), setting.downsampling_resolution, 10, setting.num_threads); + auto [source_points, source_tree] = preprocess_points(*std::make_shared(source), setting.downsampling_resolution, 10, setting.num_threads); if (setting.type == RegistrationSetting::VGICP) { auto target_voxelmap = create_gaussian_voxelmap(*target_points, setting.voxel_resolution);

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