Fix VS2022 build (OpenMP optional) and debugger working directory for examples/tests (#119)

* Pass compile on Windows

* Set debugger working diretoriy as project root for examples and tests on Windows

.github/workflows/coverage.yml

@@ -13,7 +13,7 @@ on:
 
 jobs:
   coverage:
-    runs-on: ubuntu-22.04
+    runs-on: ubuntu-24.04
 
     steps:
       - uses: actions/checkout@v2
@@ -23,7 +23,7 @@ jobs:
       - name: Install Dependencies
         run: |
           sudo apt-get -y update
-          sudo apt-get install --no-install-recommends -y build-essential cmake python3-pip pybind11-dev libeigen3-dev libfmt-dev libtbb-dev libomp-dev libpcl-dev libgtest-dev lcov
+          sudo apt-get install --no-install-recommends -y build-essential cmake python3-pip pybind11-dev libeigen3-dev libfmt-dev libtbb-dev libomp-dev libpcl-dev libgtest-dev lcov ninja-build
           pip install -U setuptools pytest pytest-cov numpy scipy
 
       - name: Build (C++)

.github/workflows/test.yml

@@ -13,7 +13,7 @@ on:
 
 jobs:
   test:
-    runs-on: ubuntu-22.04
+    runs-on: ubuntu-24.04
 
     steps:
       - uses: actions/checkout@v2
@@ -23,7 +23,7 @@ jobs:
       - name: Install Dependencies
         run: |
           sudo apt-get -y update
-          sudo apt-get install --no-install-recommends -y build-essential cmake python3-pip pybind11-dev libeigen3-dev libfmt-dev libtbb-dev libomp-dev libpcl-dev libgtest-dev
+          sudo apt-get install --no-install-recommends -y build-essential cmake python3-pip pybind11-dev libeigen3-dev libfmt-dev libtbb-dev libomp-dev libpcl-dev libgtest-dev ninja-build
           pip install -U setuptools pytest numpy scipy
 
       - name: Build

BENCHMARK.md

@@ -129,5 +129,5 @@ small_vgicp          : APE=5.956 +- 2.725  RPE(100)=1.315 +- 0.762  RPE(400)=6.8
 
 [Code](https://github.com/koide3/small_gicp/blob/pybench/src/benchmark/odometry_benchmark.py)
 
-Processing speed comparison between small_gicp and Open3D ([youtube]((https://youtu.be/LNESzGXPr4c?feature=shared))).
+Processing speed comparison between small_gicp and Open3D ([youtube](https://youtu.be/LNESzGXPr4c?feature=shared)).
 [![small_comp](https://github.com/koide3/small_gicp/assets/31344317/7959edd6-f0e4-4318-b4c1-a3f8755c407f)](https://youtu.be/LNESzGXPr4c?feature=shared)

CITATION.cff

@@ -0,0 +1,32 @@
+cff-version: "1.2.0"
+authors:
+- family-names: Koide
+  given-names: Kenji
+  orcid: "https://orcid.org/0000-0001-5361-1428"
+contact:
+- family-names: Koide
+  given-names: Kenji
+  orcid: "https://orcid.org/0000-0001-5361-1428"
+doi: 10.5281/zenodo.13283012
+message: If you use this software, please cite our article in the
+  Journal of Open Source Software.
+preferred-citation:
+  authors:
+  - family-names: Koide
+    given-names: Kenji
+    orcid: "https://orcid.org/0000-0001-5361-1428"
+  date-published: 2024-08-10
+  doi: 10.21105/joss.06948
+  issn: 2475-9066
+  issue: 100
+  journal: Journal of Open Source Software
+  publisher:
+    name: Open Journals
+  start: 6948
+  title: "small_gicp: Efficient and parallel algorithms for point cloud
+    registration"
+  type: article
+  url: "https://joss.theoj.org/papers/10.21105/joss.06948"
+  volume: 9
+title: "small_gicp: Efficient and parallel algorithms for point cloud
+  registration"

CMakeLists.txt

@@ -97,7 +97,10 @@ endif()
 
 if(MSVC)
   add_compile_definitions(_USE_MATH_DEFINES)
-  # add_compile_options(/openmp:llvm)
+  add_compile_options(/bigobj)
+  if(BUILD_WITH_OPENMP)
+    add_compile_options(/openmp:llvm)
+  endif()
 endif()
 
 ##############
@@ -111,8 +114,8 @@ if(ENABLE_COVERAGE)
   find_program(GENHTML genhtml REQUIRED)
 
   add_custom_target(coverage
-    COMMAND ${LCOV} --directory . --capture --output-file coverage.info
-    COMMAND ${LCOV} --remove coverage.info -o coverage.info '/usr/*'
+    COMMAND ${LCOV} --directory . --capture --output-file coverage.info --ignore-errors mismatch
+    COMMAND ${LCOV} --remove coverage.info -o coverage.info '/usr/*' --ignore-errors mismatch
     COMMAND ${GENHTML} --demangle-cpp -o coverage coverage.info
     WORKING_DIRECTORY ${CMAKE_BINARY_DIR})
 endif()
@@ -247,6 +250,12 @@ if(BUILD_EXAMPLES)
       TBB::tbbmalloc
       PCL::PCL
     )
+   if(MSVC)
+     set_target_properties(${EXAMPLE_NAME}
+                           PROPERTIES VS_DEBUGGER_WORKING_DIRECTORY
+                           "${CMAKE_SOURCE_DIR}"
+     )
+   endif()
   endforeach()
 endif()
 
@@ -273,6 +282,13 @@ if(BUILD_TESTS)
     )
 
     gtest_discover_tests(${TEST_NAME} WORKING_DIRECTORY ${CMAKE_SOURCE_DIR})
+
+   if(MSVC)
+     set_target_properties(${TEST_NAME}
+                           PROPERTIES VS_DEBUGGER_WORKING_DIRECTORY
+                           "${CMAKE_SOURCE_DIR}"
+     )
+   endif()
   endforeach()
 endif()
 

README.md

@@ -10,10 +10,10 @@
 
 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](https://github.com/koide3/small_gicp/issues/3). It would help the author receive recognition in his organization and keep working on this project.
+If you find this package useful for your project, please consider leaving a comment [here](https://github.com/koide3/small_gicp/issues/3). It would help the author receive recognition in his organization and keep working on this project. Please also cite [the corresponding software paper](https://joss.theoj.org/papers/10.21105/joss.06948) if you use this software in an academic work.
 
 
-[![Build(Linux)](https://github.com/koide3/small_gicp/actions/workflows/build-linux.yml/badge.svg)](https://github.com/koide3/small_gicp/actions/workflows/build-linux.yml) [![macos](https://github.com/koide3/small_gicp/actions/workflows/build-macos.yml/badge.svg)](https://github.com/koide3/small_gicp/actions/workflows/build-macos.yml) [![Build(Windows)](https://github.com/koide3/small_gicp/actions/workflows/build-windows.yml/badge.svg)](https://github.com/koide3/small_gicp/actions/workflows/build-windows.yml) [![Test](https://github.com/koide3/small_gicp/actions/workflows/test.yml/badge.svg)](https://github.com/koide3/small_gicp/actions/workflows/test.yml) [![codecov](https://codecov.io/gh/koide3/small_gicp/graph/badge.svg?token=PCVIUP2Z33)](https://codecov.io/gh/koide3/small_gicp)
+[![status](https://joss.theoj.org/papers/059b017c823ed9fd211fc91373cdc2cc/status.svg)](https://joss.theoj.org/papers/059b017c823ed9fd211fc91373cdc2cc) [![Build(Linux)](https://github.com/koide3/small_gicp/actions/workflows/build-linux.yml/badge.svg)](https://github.com/koide3/small_gicp/actions/workflows/build-linux.yml) [![macos](https://github.com/koide3/small_gicp/actions/workflows/build-macos.yml/badge.svg)](https://github.com/koide3/small_gicp/actions/workflows/build-macos.yml) [![Build(Windows)](https://github.com/koide3/small_gicp/actions/workflows/build-windows.yml/badge.svg)](https://github.com/koide3/small_gicp/actions/workflows/build-windows.yml) [![Test](https://github.com/koide3/small_gicp/actions/workflows/test.yml/badge.svg)](https://github.com/koide3/small_gicp/actions/workflows/test.yml) [![codecov](https://codecov.io/gh/koide3/small_gicp/graph/badge.svg?token=PCVIUP2Z33)](https://codecov.io/gh/koide3/small_gicp)
 
 ## Requirements
 
@@ -431,7 +431,7 @@ python3 src/example/basic_registration.py
 
 ## [Benchmark](BENCHMARK.md)
 
-Processing speed comparison between small_gicp and Open3D ([youtube]((https://youtu.be/LNESzGXPr4c?feature=shared))).
+Processing speed comparison between small_gicp and Open3D ([youtube](https://youtu.be/LNESzGXPr4c?feature=shared)).
 [![small_comp](https://github.com/koide3/small_gicp/assets/31344317/7959edd6-f0e4-4318-b4c1-a3f8755c407f)](https://youtu.be/LNESzGXPr4c?feature=shared)
 
 ### Downsampling
@@ -465,6 +465,22 @@ This package is released under the MIT license.
 
 If you find this package useful for your project, please consider leaving a comment [here](https://github.com/koide3/small_gicp/issues/3). It would help the author receive recognition in his organization and keep working on this project.
 
+Please also cite [the following article](https://joss.theoj.org/papers/10.21105/joss.06948) if you use this software in an academic work.
+
+```
+@article{small_gicp,
+author = {Kenji Koide},
+title = {{small\_gicp: Efficient and parallel algorithms for point cloud registration}},
+journal = {Journal of Open Source Software},
+month = aug,
+number = {100},
+pages = {6948},
+volume = {9},
+year = {2024},
+doi = {10.21105/joss.06948}
+}
+```
+
 ## Contact
 
 [Kenji Koide](https://staff.aist.go.jp/k.koide/), National Institute of Advanced Industrial Science and Technology (AIST)

docker/Dockerfile.pytest.gcc

@@ -7,7 +7,7 @@ ENV DEBIAN_FRONTEND=noninteractive
 RUN apt-get update && apt-get install --no-install-recommends -y \
   && apt-get install --no-install-recommends -y \
   wget nano build-essential git cmake python3-dev python3-pip pybind11-dev \
-  libeigen3-dev libomp-dev
+  libeigen3-dev libomp-dev ninja-build
 
 RUN mkdir -p ~/.config/pip
 RUN echo "[global]\nbreak-system-packages=true" > ~/.config/pip/pip.conf

docker/Dockerfile.pytest.llvm

@@ -7,7 +7,7 @@ ENV DEBIAN_FRONTEND=noninteractive
 RUN apt-get update && apt-get install --no-install-recommends -y \
   && apt-get install --no-install-recommends -y \
   wget nano build-essential git cmake python3-dev python3-pip pybind11-dev \
-  libeigen3-dev libomp-dev
+  libeigen3-dev libomp-dev ninja-build
 
 RUN apt-get update && apt-get install --no-install-recommends -y \
   && apt-get install --no-install-recommends -y \

include/small_gicp/ann/incremental_voxelmap.hpp

@@ -34,6 +34,7 @@ public:
 ///        This class supports incremental point cloud insertion and LRU-based voxel deletion that removes voxels that are not recently referenced.
 /// @note  This class can be used as a point cloud as well as a neighbor search structure.
 /// @note  This class can handle arbitrary number of voxels and arbitrary range of voxel coordinates (in 32-bit int range).
+/// @note  To use this as a source point cloud for registration, use `SequentialVoxelMapAccessor`.
 template <typename VoxelContents>
 struct IncrementalVoxelMap {
 public:

include/small_gicp/ann/projective_search.hpp

@@ -0,0 +1,219 @@
+// SPDX-FileCopyrightText: Copyright 2024 Kenji Koide
+// SPDX-License-Identifier: MIT
+#pragma once
+
+#include <cmath>
+#include <Eigen/Core>
+#include <small_gicp/points/traits.hpp>
+#include <small_gicp/ann/knn_result.hpp>
+
+namespace small_gicp {
+
+/// @brief Equirectangular projection.
+struct EquirectangularProjection {
+public:
+  /// @brief Project the point into the normalized image coordinates. (u, v) in ([0, 1], [0, 1])
+  Eigen::Vector2d operator()(const Eigen::Vector3d& pt_3d) const {
+    if (pt_3d.squaredNorm() < 1e-3) {
+      return Eigen::Vector2d(0.5, 0.5);
+    }
+
+    const Eigen::Vector3d bearing = pt_3d.normalized();
+    const double lat = -std::asin(bearing[1]);
+    const double lon = std::atan2(bearing[0], bearing[2]);
+
+    return Eigen::Vector2d(lon / (2.0 * M_PI) + 0.5, lat / M_PI + 0.5);
+  };
+};
+
+/// @brief Border clamp mode. Points out of the border are discarded.
+struct BorderClamp {
+public:
+  int operator()(int x, int width) const { return x; }
+};
+
+/// @brief Border repeat mode. Points out of the border are wrapped around.
+struct BorderRepeat {
+public:
+  int operator()(int x, int width) const { return x < 0 ? x + width : (x >= width ? x - width : x); }
+};
+
+/// @brief "Unsafe" projective search. This class does not hold the ownership of the target point cloud.
+template <typename PointCloud, typename Projection = EquirectangularProjection, typename BorderModeH = BorderRepeat, typename BorderModeV = BorderClamp>
+struct UnsafeProjectiveSearch {
+public:
+  /// @brief Constructor.
+  /// @param width    Index map width
+  /// @param height   Index map height
+  /// @param points   Target point cloud
+  UnsafeProjectiveSearch(int width, int height, const PointCloud& points) : points(points), index_map(height, width), search_window_h(10), search_window_v(5) {
+    index_map.setConstant(invalid_index);
+
+    Projection project;
+    for (size_t i = 0; i < traits::size(points); ++i) {
+      const Eigen::Vector4d pt = traits::point(points, i);
+      const Eigen::Vector2d uv = project(pt.head<3>());
+      const int u = uv[0] * index_map.cols();
+      const int v = uv[1] * index_map.rows();
+
+      if (u < 0 || u >= index_map.cols() || v < 0 || v >= index_map.rows()) {
+        continue;
+      }
+      index_map(v, u) = i;
+    }
+  }
+
+  /// @brief Find the nearest neighbor.
+  /// @param query        Query point
+  /// @param k_indices    Index of the nearest neighbor (uninitialized if not found)
+  /// @param k_sq_dists   Squared distance to the nearest neighbor (uninitialized if not found)
+  /// @param setting      KNN search setting
+  /// @return             Number of found neighbors (0 or 1)
+  size_t nearest_neighbor_search(const Eigen::Vector4d& query, size_t* k_indices, double* k_sq_dists, const KnnSetting& setting = KnnSetting()) const {
+    return knn_search<1>(query, k_indices, k_sq_dists, setting);
+  }
+
+  /// @brief  Find k-nearest neighbors. This method uses dynamic memory allocation.
+  /// @param  query       Query point
+  /// @param  k           Number of neighbors
+  /// @param  k_indices   Indices of neighbors
+  /// @param  k_sq_dists  Squared distances to neighbors (sorted in ascending order)
+  /// @param  setting     KNN search setting
+  /// @return             Number of found neighbors
+  size_t knn_search(const Eigen::Vector4d& query, int k, size_t* k_indices, double* k_sq_dists, const KnnSetting& setting = KnnSetting()) const {
+    KnnResult<-1> result(k_indices, k_sq_dists, k);
+    knn_search(query, result, setting);
+    return result.num_found();
+  }
+
+  /// @brief Find k-nearest neighbors. This method uses fixed and static memory allocation. Might be faster for small k.
+  /// @param query       Query point
+  /// @param k_indices   Indices of neighbors
+  /// @param k_sq_dists  Squared distances to neighbors (sorted in ascending order)
+  /// @param setting     KNN search setting
+  /// @return            Number of found neighbors
+  template <int N>
+  size_t knn_search(const Eigen::Vector4d& query, size_t* k_indices, double* k_sq_dists, const KnnSetting& setting = KnnSetting()) const {
+    KnnResult<N> result(k_indices, k_sq_dists);
+    knn_search(query, result, setting);
+    return result.num_found();
+  }
+
+private:
+  template <typename Result>
+  void knn_search(const Eigen::Vector4d& query, Result& result, const KnnSetting& setting) const {
+    BorderModeH border_h;
+    BorderModeV border_v;
+
+    Projection project;
+    const Eigen::Vector2d uv = project(query.head<3>());
+    const int u = uv[0] * index_map.cols();
+    const int v = uv[1] * index_map.rows();
+
+    for (int du = -search_window_h; du <= search_window_h; du++) {
+      const int u_clamped = border_h(u + du, index_map.cols());
+      if (u_clamped < 0 || u_clamped >= index_map.cols()) {
+        continue;
+      }
+
+      for (int dv = -search_window_v; dv <= search_window_v; dv++) {
+        const int v_clamped = border_v(v + dv, index_map.rows());
+        if (v_clamped < 0 || v_clamped >= index_map.rows()) {
+          continue;
+        }
+
+        const auto index = index_map(v_clamped, u_clamped);
+        if (index == invalid_index) {
+          continue;
+        }
+
+        const double sq_dist = (traits::point(points, index) - query).squaredNorm();
+        result.push(index, sq_dist);
+
+        if (setting.fulfilled(result)) {
+          return;
+        }
+      }
+    }
+  }
+
+public:
+  static constexpr std::uint32_t invalid_index = std::numeric_limits<std::uint32_t>::max();
+
+  const PointCloud& points;
+  Eigen::Matrix<std::uint32_t, -1, -1> index_map;
+
+  int search_window_h;
+  int search_window_v;
+};
+
+/// @brief "Safe" projective search. This class holds the ownership of the target point cloud.
+template <typename PointCloud, typename Projection = EquirectangularProjection, typename BorderModeH = BorderRepeat, typename BorderModeV = BorderClamp>
+struct ProjectiveSearch {
+public:
+  using Ptr = std::shared_ptr<ProjectiveSearch<PointCloud, Projection>>;
+  using ConstPtr = std::shared_ptr<const ProjectiveSearch<PointCloud, Projection>>;
+
+  explicit ProjectiveSearch(int width, int height, std::shared_ptr<const PointCloud> points) : points(points), search(width, height, *points) {}
+
+  /// @brief  Find k-nearest neighbors. This method uses dynamic memory allocation.
+  /// @param  query       Query point
+  /// @param  k           Number of neighbors
+  /// @param  k_indices   Indices of neighbors
+  /// @param  k_sq_dists  Squared distances to neighbors (sorted in ascending order)
+  /// @param  setting     KNN search setting
+  /// @return             Number of found neighbors
+  size_t nearest_neighbor_search(const Eigen::Vector4d& query, size_t* k_indices, double* k_sq_dists, const KnnSetting& setting = KnnSetting()) const {
+    return search.nearest_neighbor_search(query, k_indices, k_sq_dists, setting);
+  }
+
+  /// @brief  Find k-nearest neighbors. This method uses dynamic memory allocation.
+  /// @param  query       Query point
+  /// @param  k           Number of neighbors
+  /// @param  k_indices   Indices of neighbors
+  /// @param  k_sq_dists  Squared distances to neighbors (sorted in ascending order)
+  /// @param  setting     KNN search setting
+  /// @return             Number of found neighbors
+  size_t knn_search(const Eigen::Vector4d& query, size_t k, size_t* k_indices, double* k_sq_dists, const KnnSetting& setting = KnnSetting()) const {
+    return search.knn_search(query, k, k_indices, k_sq_dists, setting);
+  }
+
+public:
+  const std::shared_ptr<const PointCloud> points;                                         ///< Points
+  const UnsafeProjectiveSearch<PointCloud, Projection, BorderModeH, BorderModeV> search;  ///< Search
+};
+
+namespace traits {
+
+template <typename PointCloud, typename Projection, typename BorderModeH, typename BorderModeV>
+struct Traits<UnsafeProjectiveSearch<PointCloud, Projection, BorderModeH, BorderModeV>> {
+  static size_t nearest_neighbor_search(
+    const UnsafeProjectiveSearch<PointCloud, Projection, BorderModeH, BorderModeV>& tree,
+    const Eigen::Vector4d& point,
+    size_t* k_indices,
+    double* k_sq_dists) {
+    return tree.nearest_neighbor_search(point, k_indices, k_sq_dists);
+  }
+
+  static size_t
+  knn_search(const UnsafeProjectiveSearch<PointCloud, Projection, BorderModeH, BorderModeV>& tree, const Eigen::Vector4d& point, size_t k, size_t* k_indices, double* k_sq_dists) {
+    return tree.knn_search(point, k, k_indices, k_sq_dists);
+  }
+};
+
+template <typename PointCloud, typename Projection, typename BorderModeH, typename BorderModeV>
+struct Traits<ProjectiveSearch<PointCloud, Projection, BorderModeH, BorderModeV>> {
+  static size_t
+  nearest_neighbor_search(const ProjectiveSearch<PointCloud, Projection, BorderModeH, BorderModeV>& tree, const Eigen::Vector4d& point, size_t* k_indices, double* k_sq_dists) {
+    return tree.nearest_neighbor_search(point, k_indices, k_sq_dists);
+  }
+
+  static size_t
+  knn_search(const ProjectiveSearch<PointCloud, Projection, BorderModeH, BorderModeV>& tree, const Eigen::Vector4d& point, size_t k, size_t* k_indices, double* k_sq_dists) {
+    return tree.knn_search(point, k, k_indices, k_sq_dists);
+  }
+};
+
+}  // namespace traits
+
+}  // namespace small_gicp

include/small_gicp/ann/sequential_voxelmap_accessor.hpp

@@ -0,0 +1,58 @@
+// SPDX-FileCopyrightText: Copyright 2024 Kenji Koide
+// SPDX-License-Identifier: MIT
+#pragma once
+
+#include <small_gicp/points/traits.hpp>
+#include <small_gicp/ann/incremental_voxelmap.hpp>
+
+namespace small_gicp {
+
+/**
+ * @brief A wrapper class to sequentially access points in a voxelmap (e.g., using points in a voxelmap as source point cloud for registration).
+ * @note  If the contents of the voxelmap are changed, the accessor must be recreated.
+ * @example
+    small_gicp::IncrementalVoxelMap<small_gicp::FlatContainerCov>::Ptr last_voxelmap = ...;
+    small_gicp::IncrementalVoxelMap<small_gicp::FlatContainerCov>::Ptr voxelmap = ...;
+
+    // Create a sequential accessor
+    const auto accessor = small_gicp::create_sequential_accessor(*voxelmap);
+
+    // Use the voxelmap as a source point cloud
+    small_gicp::Registration<small_gicp::GICPFactor, small_gicp::ParallelReductionOMP> registration;
+    auto result = registration.align(*last_voxelmap, accessor, *last_voxelmap, Eigen::Isometry3d::Identity());
+ */
+template <typename VoxelMap>
+class SequentialVoxelMapAccessor {
+public:
+  /// @brief Constructor.
+  /// @param voxelmap Voxelmap
+  SequentialVoxelMapAccessor(const VoxelMap& voxelmap) : voxelmap(voxelmap), indices(traits::point_indices(voxelmap)) {}
+
+  /// @brief Number of points in the voxelmap.
+  size_t size() const { return indices.size(); }
+
+public:
+  const VoxelMap& voxelmap;           ///< Voxelmap
+  const std::vector<size_t> indices;  ///< Indices of points in the voxelmap
+};
+
+/// @brief Create a sequential voxelmap accessor.
+template <typename VoxelMap>
+SequentialVoxelMapAccessor<VoxelMap> create_sequential_accessor(const VoxelMap& voxelmap) {
+  return SequentialVoxelMapAccessor<VoxelMap>(voxelmap);
+}
+
+template <typename VoxelMap>
+struct traits::Traits<SequentialVoxelMapAccessor<VoxelMap>> {
+  static size_t size(const SequentialVoxelMapAccessor<VoxelMap>& accessor) { return accessor.size(); }
+
+  static bool has_points(const SequentialVoxelMapAccessor<VoxelMap>& accessor) { return traits::has_points(accessor.voxelmap); }
+  static bool has_normals(const SequentialVoxelMapAccessor<VoxelMap>& accessor) { return traits::has_normals(accessor.voxelmap); }
+  static bool has_covs(const SequentialVoxelMapAccessor<VoxelMap>& accessor) { return traits::has_covs(accessor.voxelmap); }
+
+  static Eigen::Vector4d point(const SequentialVoxelMapAccessor<VoxelMap>& accessor, size_t i) { return traits::point(accessor.voxelmap, accessor.indices[i]); }
+  static Eigen::Vector4d normal(const SequentialVoxelMapAccessor<VoxelMap>& accessor, size_t i) { return traits::normal(accessor.voxelmap, accessor.indices[i]); }
+  static Eigen::Matrix4d cov(const SequentialVoxelMapAccessor<VoxelMap>& accessor, size_t i) { return traits::cov(accessor.voxelmap, accessor.indices[i]); }
+};
+
+}  // namespace small_gicp

include/small_gicp/pcl/pcl_registration.hpp

@@ -99,8 +99,8 @@ protected:
   std::string registration_type_;  ///< Registration type ("GICP" or "VGICP").
   bool verbose_;                   ///< Verbosity flag.
 
-  std::shared_ptr<KdTree<pcl::PointCloud<PointSource>>> target_tree_;  ///< KdTree for target point cloud.
-  std::shared_ptr<KdTree<pcl::PointCloud<PointSource>>> source_tree_;  ///< KdTree for source point cloud.
+  std::shared_ptr<small_gicp::KdTree<pcl::PointCloud<PointSource>>> target_tree_;  ///< KdTree for target point cloud.
+  std::shared_ptr<small_gicp::KdTree<pcl::PointCloud<PointSource>>> source_tree_;  ///< KdTree for source point cloud.
 
   std::shared_ptr<GaussianVoxelMap> target_voxelmap_;  ///< VoxelMap for target point cloud.
   std::shared_ptr<GaussianVoxelMap> source_voxelmap_;  ///< VoxelMap for source point cloud.

include/small_gicp/pcl/pcl_registration_impl.hpp

@@ -44,7 +44,7 @@ void RegistrationPCL<PointSource, PointTarget>::setInputSource(const PointCloudS
   }
 
   pcl::Registration<PointSource, PointTarget, Scalar>::setInputSource(cloud);
-  source_tree_ = std::make_shared<KdTree<pcl::PointCloud<PointSource>>>(input_, KdTreeBuilderOMP(num_threads_));
+  source_tree_ = std::make_shared<small_gicp::KdTree<pcl::PointCloud<PointSource>>>(input_, KdTreeBuilderOMP(num_threads_));
   source_covs_.clear();
   source_voxelmap_.reset();
 }
@@ -56,7 +56,7 @@ void RegistrationPCL<PointSource, PointTarget>::setInputTarget(const PointCloudT
   }
 
   pcl::Registration<PointSource, PointTarget, Scalar>::setInputTarget(cloud);
-  target_tree_ = std::make_shared<KdTree<pcl::PointCloud<PointTarget>>>(target_, KdTreeBuilderOMP(num_threads_));
+  target_tree_ = std::make_shared<small_gicp::KdTree<pcl::PointCloud<PointTarget>>>(target_, KdTreeBuilderOMP(num_threads_));
   target_covs_.clear();
   target_voxelmap_.reset();
 }
@@ -214,7 +214,7 @@ void RegistrationPCL<PointSource, PointTarget>::computeTransformation(PointCloud
     estimate_covariances_omp(target_proxy, *target_tree_, k_correspondences_, num_threads_);
   }
 
-  Registration<GICPFactor, ParallelReductionOMP> registration;
+  small_gicp::Registration<GICPFactor, ParallelReductionOMP> registration;
   registration.criteria.rotation_eps = rotation_epsilon_;
   registration.criteria.translation_eps = transformation_epsilon_;
   registration.reduction.num_threads = num_threads_;

include/small_gicp/points/eigen.hpp

@@ -2,6 +2,7 @@
 // SPDX-License-Identifier: MIT
 #pragma once
 
+#include <vector>
 #include <Eigen/Core>
 #include <small_gicp/points/traits.hpp>
 
@@ -15,5 +16,30 @@ struct Traits<Eigen::MatrixXd> {
   static Eigen::Vector4d point(const Eigen::MatrixXd& points, size_t i) { return Eigen::Vector4d(points(i, 0), points(i, 1), points(i, 2), 1.0); }
 };
 
+template <typename Scalar, int D>
+struct Traits<std::vector<Eigen::Matrix<Scalar, D, 1>>> {
+  static_assert(D == 3 || D == 4, "Only 3D and 4D (homogeneous) points are supported");
+  static_assert(std::is_floating_point<Scalar>::value, "Only floating point types are supported");
+
+  using Point = Eigen::Matrix<Scalar, D, 1>;
+  static size_t size(const std::vector<Point>& points) { return points.size(); }
+  static bool has_points(const std::vector<Point>& points) { return points.size(); }
+  static Eigen::Vector4d point(const std::vector<Point>& points, size_t i) {
+    if constexpr (std::is_same_v<Scalar, double>) {
+      if constexpr (D == 3) {
+        return points[i].homogeneous();
+      } else {
+        return points[i];
+      }
+    } else {
+      if constexpr (D == 3) {
+        return points[i].homogeneous().template cast<double>();
+      } else {
+        return points[i].template cast<double>();
+      }
+    }
+  }
+};
+
 }  // namespace traits
 }  // namespace small_gicp

include/small_gicp/registration/optimizer.hpp

@@ -108,7 +108,7 @@ struct LevenbergMarquardtOptimizer {
         // Solve with damping
         const Eigen::Matrix<double, 6, 1> delta = (H + lambda * Eigen::Matrix<double, 6, 6>::Identity()).ldlt().solve(-b);
 
-        // Validte new solution
+        // Validate new solution
         const Eigen::Isometry3d new_T = result.T_target_source * se3_exp(delta);
         double new_e = reduction.error(target, source, new_T, factors);
         general_factor.update_error(target, source, new_T, &e);
@@ -124,6 +124,7 @@ struct LevenbergMarquardtOptimizer {
           result.T_target_source = new_T;
           lambda /= lambda_factor;
           success = true;
+          e = new_e;
 
           break;
         } else {

include/small_gicp/registration/reduction_omp.hpp

@@ -7,7 +7,11 @@
 namespace small_gicp {
 
 #ifndef _OPENMP
+#ifdef _WIN32
+#pragma message(__FILE__, " OpenMP is not available.Parallel reduction will be disabled.")
+#else
 #warning "OpenMP is not available. Parallel reduction will be disabled."
+#endif
 inline int omp_get_thread_num() {
   return 0;
 }

src/python/align.cpp

@@ -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;
@@ -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());
@@ -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.
@@ -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.
 
@@ -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") {
@@ -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) {
@@ -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.
@@ -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.
 
@@ -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));
@@ -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.
@@ -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.
 

src/python/kdtree.cpp

@@ -129,7 +129,7 @@ void define_kdtree(py::module& m) {
         std::vector<size_t> k_indices(pts.rows(), -1);
         std::vector<double> k_sq_dists(pts.rows(), std::numeric_limits<double>::max());
 
-#pragma omp parallel for num_threads(num_threads)
+#pragma omp parallel for num_threads(num_threads) schedule(guided, 4)
         for (int i = 0; i < pts.rows(); ++i) {
           const size_t found = traits::nearest_neighbor_search(kdtree, Eigen::Vector4d(pts(i, 0), pts(i, 1), pts(i, 2), 1.0), &k_indices[i], &k_sq_dists[i]);
           if (!found) {
@@ -154,9 +154,9 @@ void define_kdtree(py::module& m) {
 
         Returns
         -------
-        k_indices : numpy.ndarray, shape (n,)
+        k_indices : numpy.ndarray, shape (n, k)
             The indices of the nearest neighbors for each input point. If a neighbor was not found, the index is -1.
-        k_sq_dists : numpy.ndarray, shape (n,)
+        k_sq_dists : numpy.ndarray, shape (n, k)
             The squared distances to the nearest neighbors for each input point.
      )""")
 
@@ -167,16 +167,21 @@ void define_kdtree(py::module& m) {
           throw std::invalid_argument("pts must have shape (n, 3) or (n, 4)");
         }
 
-        std::vector<std::vector<size_t>> k_indices(pts.rows(), std::vector<size_t>(k, -1));
-        std::vector<std::vector<double>> k_sq_dists(pts.rows(), std::vector<double>(k, std::numeric_limits<double>::max()));
+        Eigen::Matrix<size_t, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor> k_indices(pts.rows(), k);
+        Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor> k_sq_dists(pts.rows(), k);
+        k_indices.setConstant(-1);
+        k_sq_dists.setConstant(std::numeric_limits<double>::max());
 
-#pragma omp parallel for num_threads(num_threads)
+#pragma omp parallel for num_threads(num_threads) schedule(guided, 4)
         for (int i = 0; i < pts.rows(); ++i) {
-          const size_t found = traits::knn_search(kdtree, Eigen::Vector4d(pts(i, 0), pts(i, 1), pts(i, 2), 1.0), k, k_indices[i].data(), k_sq_dists[i].data());
+          size_t* k_indices_begin = k_indices.data() + i * k;
+          double* k_sq_dists_begin = k_sq_dists.data() + i * k;
+
+          const size_t found = traits::knn_search(kdtree, Eigen::Vector4d(pts(i, 0), pts(i, 1), pts(i, 2), 1.0), k, k_indices_begin, k_sq_dists_begin);
           if (found < k) {
             for (size_t j = found; j < k; ++j) {
-              k_indices[i][j] = -1;
-              k_sq_dists[i][j] = std::numeric_limits<double>::max();
+              k_indices_begin[j] = -1;
+              k_sq_dists_begin[j] = std::numeric_limits<double>::max();
             }
           }
         }