Namespaces
	traits

Classes
struct	FlatContainer
	Point container with a flat vector. More...

struct	GaussianVoxel
	Gaussian voxel that computes and stores voxel mean and covariance. More...

struct	VoxelInfo
	Voxel meta information. More...

struct	IncrementalVoxelMap
	Incremental voxelmap. This class supports incremental point cloud insertion and LRU-based voxel deletion that removes voxels that are not recently referenced. More...

struct	KdTreeNode
	KdTree node. More...

struct	KdTreeBuilder
	Single thread Kd-tree builder. More...

struct	UnsafeKdTree
	"Unsafe" KdTree. More...

struct	KdTree
	"Safe" KdTree that holds the ownership of the input points. More...

struct	KdTreeBuilderOMP
	Kd-tree builder with OpenMP. More...

struct	KdTreeBuilderTBB
	Kd-tree builder with TBB. More...

struct	KnnSetting
	K-nearest neighbor search setting. More...

struct	identity_transform
	Identity transform (alternative to std::identity in C++20). More...

struct	KnnResult
	K-nearest neighbor search result container. More...

struct	ProjectionSetting
	Parameters to control the projection axis search. More...

struct	AxisAlignedProjection
	Conventional axis-aligned projection (i.e., selecting any of XYZ axes with the largest variance). More...

struct	NormalProjection
	Normal projection (i.e., selecting the 3D direction with the largest variance of the points). More...

struct	NullFactor
	Null factor that gives no constraints. More...

struct	RestrictDoFFactor
	Factor to restrict the degrees of freedom of optimization (e.g., fixing roll, pitch rotation). More...

struct	GICPFactor
	GICP (distribution-to-distribution) per-point error factor. More...

struct	ICPFactor
	Point-to-point per-point error factor. More...

struct	PointToPlaneICPFactor
	Point-to-plane per-point error factor. More...

struct	Huber
	Huber robust kernel. More...

struct	Cauchy
	Cauchy robust kernel. More...

struct	RobustFactor
	Robustify a factor with a robust kernel. More...

struct	PointCloudProxy
	Proxy class to access PCL point cloud with external covariance matrices. More...

class	RegistrationPCL
	PCL registration interfaces. More...

struct	PointCloud
	Point cloud. More...

struct	GaussNewtonOptimizer
	GaussNewton optimizer. More...

struct	LevenbergMarquardtOptimizer
	LevenbergMarquardt optimizer. More...

struct	SerialReduction
	Single-thread reduction. More...

struct	ParallelReductionOMP
	Parallel reduction with OpenMP backend. More...

struct	LinearizeSum
	Summation for linearized systems. More...

struct	ErrorSum
	Summation for evaluated errors. More...

struct	ParallelReductionTBB
	Parallel reduction with TBB backend. More...

struct	Registration
	Point cloud registration. More...

struct	RegistrationSetting
	Registration setting. More...

struct	RegistrationResult
	Registration result. More...

struct	NullRejector
	Null correspondence rejector. This class accepts all input correspondences. More...

struct	DistanceRejector
	Rejecting correspondences with large distances. More...

struct	TerminationCriteria
	Registration termination criteria. More...

struct	NormalSetter
	Computes point normals from eigenvectors and sets them to the point cloud. More...

struct	CovarianceSetter
	Computes point covariances from eigenvectors and sets them to the point cloud. More...

struct	NormalCovarianceSetter
	Computes point normals and covariances from eigenvectors and sets them to the point cloud. More...

struct	RadixSortBuffers
	Temporal buffers for radix sort. More...

struct	XORVector3iHash
	Spatial hashing function. Teschner et al., "Optimized Spatial Hashing for Collision Detection of Deformable Objects", VMV2003. More...

Typedefs
using	FlatContainerPoints = FlatContainer< false, false >
	FlatContainer that stores only points. More...

using	FlatContainerNormal = FlatContainer< true, false >
	FlatContainer with normals. More...

using	FlatContainerCov = FlatContainer< false, true >
	FlatContainer with covariances. More...

using	FlatContainerNormalCov = FlatContainer< true, true >
	FlatContainer with normals and covariances. More...

using	GaussianVoxelMap = IncrementalVoxelMap< GaussianVoxel >

using	NodeIndexType = std::uint32_t

Functions
int	omp_get_thread_num ()

std::pair< PointCloud::Ptr, std::shared_ptr< KdTree< PointCloud > > >	preprocess_points (const PointCloud &points, double downsampling_resolution, int num_neighbors=10, int num_threads=4)
	Preprocess point cloud (downsampling, kdtree creation, and normal and covariance estimation). More...

template<typename T , int D>
std::pair< PointCloud::Ptr, std::shared_ptr< KdTree< PointCloud > > >	preprocess_points (const std::vector< Eigen::Matrix< T, D, 1 >> &points, double downsampling_resolution, int num_neighbors=10, int num_threads=4)
	Preprocess point cloud (downsampling, kdtree creation, and normal and covariance estimation) More...

GaussianVoxelMap::Ptr	create_gaussian_voxelmap (const PointCloud &points, double voxel_resolution)
	Create an incremental Gaussian voxel map. More...

template<typename T , int D>
RegistrationResult	align (const std::vector< Eigen::Matrix< T, D, 1 >> &target, const std::vector< Eigen::Matrix< T, D, 1 >> &source, const Eigen::Isometry3d &init_T=Eigen::Isometry3d::Identity(), const RegistrationSetting &setting=RegistrationSetting())
	Align point clouds. More...

RegistrationResult	align (const PointCloud &target, const PointCloud &source, const KdTree< PointCloud > &target_tree, const Eigen::Isometry3d &init_T=Eigen::Isometry3d::Identity(), const RegistrationSetting &setting=RegistrationSetting())
	Align preprocessed point clouds. More...

RegistrationResult	align (const GaussianVoxelMap &target, const PointCloud &source, const Eigen::Isometry3d &init_T=Eigen::Isometry3d::Identity(), const RegistrationSetting &setting=RegistrationSetting())
	Align preprocessed point clouds with VGICP. More...

template<typename InputPointCloud , typename OutputPointCloud = InputPointCloud>
std::shared_ptr< OutputPointCloud >	voxelgrid_sampling (const InputPointCloud &points, double leaf_size)
	Voxelgrid downsampling. This function computes exact average of points in each voxel, and each voxel can contain arbitrary number of points. More...

template<typename InputPointCloud , typename OutputPointCloud = InputPointCloud, typename RNG = std::mt19937>
std::shared_ptr< OutputPointCloud >	random_sampling (const InputPointCloud &points, size_t num_samples, RNG &rng)
	Random downsampling. More...

template<typename InputPointCloud , typename OutputPointCloud = InputPointCloud>
std::shared_ptr< OutputPointCloud >	voxelgrid_sampling_omp (const InputPointCloud &points, double leaf_size, int num_threads=4)
	Voxel grid downsampling with OpenMP backend. More...

template<typename InputPointCloud , typename OutputPointCloud = InputPointCloud>
std::shared_ptr< OutputPointCloud >	voxelgrid_sampling_tbb (const InputPointCloud &points, double leaf_size)
	Voxel grid downsampling with TBB backend. More...

Eigen::Array4i	fast_floor (const Eigen::Array4d &pt)
	Fast floor (https://stackoverflow.com/questions/824118/why-is-floor-so-slow). More...

Eigen::Matrix3d	skew (const Eigen::Vector3d &x)
	Create skew symmetric matrix. More...

Eigen::Quaterniond	so3_exp (const Eigen::Vector3d &omega)
	SO3 expmap. More...

Eigen::Isometry3d	se3_exp (const Eigen::Matrix< double, 6, 1 > &a)
	SE3 expmap (Rotation-first). More...

template<typename Setter , typename PointCloud , typename Tree >
void	estimate_local_features (PointCloud &cloud, Tree &kdtree, int num_neighbors, size_t point_index)

template<typename Setter , typename PointCloud , typename KdTree >
void	estimate_local_features (PointCloud &cloud, const KdTree &kdtree, int num_neighbors)

template<typename Setter , typename PointCloud >
void	estimate_local_features (PointCloud &cloud, int num_neighbors)

template<typename PointCloud >
void	estimate_normals (PointCloud &cloud, int num_neighbors=20)
	Estimate point normals. More...

template<typename PointCloud , typename KdTree >
void	estimate_normals (PointCloud &cloud, KdTree &kdtree, int num_neighbors=20)
	Estimate point normals. More...

template<typename PointCloud >
void	estimate_covariances (PointCloud &cloud, int num_neighbors=20)
	Estimate point covariances. More...

template<typename PointCloud , typename KdTree >
void	estimate_covariances (PointCloud &cloud, KdTree &kdtree, int num_neighbors=20)
	Estimate point covariances. More...

template<typename PointCloud >
void	estimate_normals_covariances (PointCloud &cloud, int num_neighbors=20)
	Estimate point normals and covariances. More...

template<typename PointCloud , typename KdTree >
void	estimate_normals_covariances (PointCloud &cloud, KdTree &kdtree, int num_neighbors=20)
	Estimate point normals and covariances. More...

template<typename Setter , typename PointCloud >
void	estimate_local_features_omp (PointCloud &cloud, int num_neighbors, int num_threads)

template<typename Setter , typename PointCloud , typename KdTree >
void	estimate_local_features_omp (PointCloud &cloud, KdTree &kdtree, int num_neighbors, int num_threads)

template<typename PointCloud >
void	estimate_normals_omp (PointCloud &cloud, int num_neighbors=20, int num_threads=4)
	Estimate point normals with OpenMP. More...

template<typename PointCloud , typename KdTree >
void	estimate_normals_omp (PointCloud &cloud, KdTree &kdtree, int num_neighbors=20, int num_threads=4)
	Estimate point normals with OpenMP. More...

template<typename PointCloud >
void	estimate_covariances_omp (PointCloud &cloud, int num_neighbors=20, int num_threads=4)
	Estimate point covariances with OpenMP. More...

template<typename PointCloud , typename KdTree >
void	estimate_covariances_omp (PointCloud &cloud, KdTree &kdtree, int num_neighbors=20, int num_threads=4)
	Estimate point covariances with OpenMP. More...

template<typename PointCloud >
void	estimate_normals_covariances_omp (PointCloud &cloud, int num_neighbors=20, int num_threads=4)
	Estimate point normals and covariances with OpenMP. More...

template<typename PointCloud , typename KdTree >
void	estimate_normals_covariances_omp (PointCloud &cloud, KdTree &kdtree, int num_neighbors=20, int num_threads=4)
	Estimate point normals and covariances with OpenMP. More...

template<typename Setter , typename PointCloud , typename KdTree >
void	estimate_local_features_tbb (PointCloud &cloud, KdTree &kdtree, int num_neighbors)

template<typename Setter , typename PointCloud >
void	estimate_local_features_tbb (PointCloud &cloud, int num_neighbors)

template<typename PointCloud >
void	estimate_normals_tbb (PointCloud &cloud, int num_neighbors=20)
	Estimate point normals with TBB. More...

template<typename PointCloud , typename KdTree >
void	estimate_normals_tbb (PointCloud &cloud, KdTree &kdtree, int num_neighbors=20)
	Estimate point normals with TBB. More...

template<typename PointCloud >
void	estimate_covariances_tbb (PointCloud &cloud, int num_neighbors=20)
	Estimate point covariances with TBB. More...

template<typename PointCloud , typename KdTree >
void	estimate_covariances_tbb (PointCloud &cloud, KdTree &kdtree, int num_neighbors=20)
	Estimate point covariances with TBB. More...

template<typename PointCloud >
void	estimate_normals_covariances_tbb (PointCloud &cloud, int num_neighbors=20)
	Estimate point normals and covariances with TBB. More...

template<typename PointCloud , typename KdTree >
void	estimate_normals_covariances_tbb (PointCloud &cloud, KdTree &kdtree, int num_neighbors=20)
	Estimate point normals and covariances with TBB. More...

template<typename RandomAccessIterator , typename Compare >
void	merge_sort_omp_impl (RandomAccessIterator first, RandomAccessIterator last, const Compare &comp)
	Implementation of merge sort with OpenMP parallelism. Do not call this directly. Use merge_sort_omp instead. More...

template<typename RandomAccessIterator , typename Compare >
void	merge_sort_omp (RandomAccessIterator first, RandomAccessIterator last, const Compare &comp, int num_threads)
	Merge sort with OpenMP parallelism. More...

template<typename RandomAccessIterator , typename Compare >
void	quick_sort_omp_impl (RandomAccessIterator first, RandomAccessIterator last, const Compare &comp)
	Implementation of quick sort with OpenMP parallelism. Do not call this directly. Use quick_sort_omp instead. More...

template<typename RandomAccessIterator , typename Compare >
void	quick_sort_omp (RandomAccessIterator first, RandomAccessIterator last, const Compare &comp, int num_threads)
	Quick sort with OpenMP parallelism. More...

template<typename T , typename KeyFunc , int bits = 8, int tile_size = 256>
void	radix_sort_tbb (T first_, T last_, const KeyFunc &key_, RadixSortBuffers< T > &buffers)
	Radix sort with TBB parallelization. More...

template<typename T , typename KeyFunc , int bits = 4, int tile_size = 256>
void	radix_sort_tbb (T first_, T last_, const KeyFunc &key_)
	Radix sort with TBB parallelization. More...

Typedef Documentation

◆ FlatContainerCov

using small_gicp::FlatContainerCov = typedef FlatContainer<false, true>

FlatContainer with covariances.

◆ FlatContainerNormal

using small_gicp::FlatContainerNormal = typedef FlatContainer<true, false>

FlatContainer with normals.

◆ FlatContainerNormalCov

using small_gicp::FlatContainerNormalCov = typedef FlatContainer<true, true>

FlatContainer with normals and covariances.

◆ FlatContainerPoints

using small_gicp::FlatContainerPoints = typedef FlatContainer<false, false>

FlatContainer that stores only points.

◆ GaussianVoxelMap

using small_gicp::GaussianVoxelMap = typedef IncrementalVoxelMap<GaussianVoxel>

◆ NodeIndexType

using small_gicp::NodeIndexType = typedef std::uint32_t

Function Documentation

◆ align() [1/3]

RegistrationResult small_gicp::align	(	const GaussianVoxelMap &	target,
		const PointCloud &	source,
		const Eigen::Isometry3d &	init_T = `Eigen::Isometry3d::Identity()`,
		const RegistrationSetting &	setting = `RegistrationSetting()`
	)

Align preprocessed point clouds with VGICP.

Parameters

target	Target Gaussian voxelmap
source	Source point cloud
init_T	Initial guess of T_target_source
setting	Registration setting

Returns: Registration result

◆ align() [2/3]

RegistrationResult small_gicp::align	(	const PointCloud &	target,
		const PointCloud &	source,
		const KdTree< PointCloud > &	target_tree,
		const Eigen::Isometry3d &	init_T = `Eigen::Isometry3d::Identity()`,
		const RegistrationSetting &	setting = `RegistrationSetting()`
	)

Align preprocessed point clouds.

Parameters

target	Target point cloud
source	Source point cloud
target_tree	Nearest neighbor search for the target point cloud
init_T	Initial guess of T_target_source
setting	Registration setting

Returns: Registration result

◆ align() [3/3]

template<typename T , int D>

RegistrationResult small_gicp::align	(	const std::vector< Eigen::Matrix< T, D, 1 >> &	target,
		const std::vector< Eigen::Matrix< T, D, 1 >> &	source,
		const Eigen::Isometry3d &	init_T = `Eigen::Isometry3d::Identity()`,
		const RegistrationSetting &	setting = `RegistrationSetting()`
	)

Align point clouds.

Note: This function only accepts Eigen::Vector(3|4)(f|d) as input

See also: small_gicp::voxelgrid_sampling_omp, small_gicp::estimate_normals_covariances_omp

Parameters

target	Target points
source	Source points
init_T	Initial guess of T_target_source
setting	Registration setting

Returns: Registration result

◆ create_gaussian_voxelmap()

GaussianVoxelMap::Ptr small_gicp::create_gaussian_voxelmap	(	const PointCloud &	points,
		double	voxel_resolution
	)

Create an incremental Gaussian voxel map.

See also: small_gicp::IncrementalVoxelMap

Parameters

points	Input points
voxel_resolution	Voxel resolution

◆ estimate_covariances() [1/2]

template<typename PointCloud >

void small_gicp::estimate_covariances	(	PointCloud &	cloud,
		int	num_neighbors = `20`
	)

Estimate point covariances.

Note: If a sufficient number of neighbor points for normal/covariance estimation (5 points) is not found, an invalid normal/covariance is set to the point (normal=zero vector, covariance=identity matrix).

Parameters

cloud	[in/out] Point cloud
num_neighbors	Number of neighbors used for attribute estimation

◆ estimate_covariances() [2/2]

template<typename PointCloud , typename KdTree >

void small_gicp::estimate_covariances	(	PointCloud &	cloud,
		KdTree &	kdtree,
		int	num_neighbors = `20`
	)

Estimate point covariances.

Note: If a sufficient number of neighbor points for normal/covariance estimation (5 points) is not found, an invalid normal/covariance is set to the point (normal=zero vector, covariance=identity matrix).

Parameters

cloud	[in/out] Point cloud
kdtree	Nearest neighbor search
num_neighbors	Number of neighbors used for attribute estimation

◆ estimate_covariances_omp() [1/2]

template<typename PointCloud >

void small_gicp::estimate_covariances_omp	(	PointCloud &	cloud,
		int	num_neighbors = `20`,
		int	num_threads = `4`
	)

Estimate point covariances with OpenMP.

Note: If a sufficient number of neighbor points for normal/covariance estimation (5 points) is not found, an invalid normal/covariance is set to the point (normal=zero vector, covariance=identity matrix).

Parameters

cloud	[in/out] Point cloud
num_neighbors	Number of neighbors used for attribute estimation
num_threads	Number of threads

◆ estimate_covariances_omp() [2/2]

template<typename PointCloud , typename KdTree >

void small_gicp::estimate_covariances_omp	(	PointCloud &	cloud,
		KdTree &	kdtree,
		int	num_neighbors = `20`,
		int	num_threads = `4`
	)

Estimate point covariances with OpenMP.

Note: If a sufficient number of neighbor points for normal/covariance estimation (5 points) is not found, an invalid normal/covariance is set to the point (normal=zero vector, covariance=identity matrix).

Parameters

cloud	[in/out] Point cloud
kdtree	Nearest neighbor search
num_neighbors	Number of neighbors used for attribute estimation
num_threads	Number of threads

◆ estimate_covariances_tbb() [1/2]

template<typename PointCloud >

void small_gicp::estimate_covariances_tbb	(	PointCloud &	cloud,
		int	num_neighbors = `20`
	)

Estimate point covariances with TBB.

Note: If a sufficient number of neighbor points for normal/covariance estimation (5 points) is not found, an invalid normal/covariance is set to the point (normal=zero vector, covariance=identity matrix).

Parameters

cloud	[in/out] Point cloud
num_neighbors	Number of neighbors used for attribute estimation

◆ estimate_covariances_tbb() [2/2]

template<typename PointCloud , typename KdTree >

void small_gicp::estimate_covariances_tbb	(	PointCloud &	cloud,
		KdTree &	kdtree,
		int	num_neighbors = `20`
	)

Estimate point covariances with TBB.

Note: If a sufficient number of neighbor points for normal/covariance estimation (5 points) is not found, an invalid normal/covariance is set to the point (normal=zero vector, covariance=identity matrix).

Parameters

cloud	[in/out] Point cloud
kdtree	Nearest neighbor search
num_neighbors	Number of neighbors used for attribute estimation

◆ estimate_local_features() [1/3]

template<typename Setter , typename PointCloud , typename KdTree >

void small_gicp::estimate_local_features	(	PointCloud &	cloud,
		const KdTree &	kdtree,
		int	num_neighbors
	)

◆ estimate_local_features() [2/3]

template<typename Setter , typename PointCloud >

void small_gicp::estimate_local_features	(	PointCloud &	cloud,
		int	num_neighbors
	)

◆ estimate_local_features() [3/3]

template<typename Setter , typename PointCloud , typename Tree >

void small_gicp::estimate_local_features	(	PointCloud &	cloud,
		Tree &	kdtree,
		int	num_neighbors,
		size_t	point_index
	)

◆ estimate_local_features_omp() [1/2]

template<typename Setter , typename PointCloud >

void small_gicp::estimate_local_features_omp	(	PointCloud &	cloud,
		int	num_neighbors,
		int	num_threads
	)

◆ estimate_local_features_omp() [2/2]

template<typename Setter , typename PointCloud , typename KdTree >

void small_gicp::estimate_local_features_omp	(	PointCloud &	cloud,
		KdTree &	kdtree,
		int	num_neighbors,
		int	num_threads
	)

◆ estimate_local_features_tbb() [1/2]

template<typename Setter , typename PointCloud >

void small_gicp::estimate_local_features_tbb	(	PointCloud &	cloud,
		int	num_neighbors
	)

◆ estimate_local_features_tbb() [2/2]

template<typename Setter , typename PointCloud , typename KdTree >

void small_gicp::estimate_local_features_tbb	(	PointCloud &	cloud,
		KdTree &	kdtree,
		int	num_neighbors
	)

◆ estimate_normals() [1/2]

template<typename PointCloud >

void small_gicp::estimate_normals	(	PointCloud &	cloud,
		int	num_neighbors = `20`
	)

Estimate point normals.

Note: If a sufficient number of neighbor points for normal/covariance estimation (5 points) is not found, an invalid normal/covariance is set to the point (normal=zero vector, covariance=identity matrix).

Parameters

cloud	[in/out] Point cloud
num_neighbors	Number of neighbors used for attribute estimation

◆ estimate_normals() [2/2]

template<typename PointCloud , typename KdTree >

void small_gicp::estimate_normals	(	PointCloud &	cloud,
		KdTree &	kdtree,
		int	num_neighbors = `20`
	)

Estimate point normals.

Note: If a sufficient number of neighbor points for normal/covariance estimation (5 points) is not found, an invalid normal/covariance is set to the point (normal=zero vector, covariance=identity matrix).

Parameters

cloud	[in/out] Point cloud
kdtree	Nearest neighbor search
num_neighbors	Number of neighbors used for attribute estimation

◆ estimate_normals_covariances() [1/2]

template<typename PointCloud >

void small_gicp::estimate_normals_covariances	(	PointCloud &	cloud,
		int	num_neighbors = `20`
	)

Estimate point normals and covariances.

Note: If a sufficient number of neighbor points for normal/covariance estimation (5 points) is not found, an invalid normal/covariance is set to the point (normal=zero vector, covariance=identity matrix).

Parameters

cloud	[in/out] Point cloud
num_neighbors	Number of neighbors used for attribute estimation

◆ estimate_normals_covariances() [2/2]

template<typename PointCloud , typename KdTree >

void small_gicp::estimate_normals_covariances	(	PointCloud &	cloud,
		KdTree &	kdtree,
		int	num_neighbors = `20`
	)

Estimate point normals and covariances.

Note: If a sufficient number of neighbor points for normal/covariance estimation (5 points) is not found, an invalid normal/covariance is set to the point (normal=zero vector, covariance=identity matrix).

Parameters

cloud	[in/out] Point cloud
kdtree	Nearest neighbor search
num_neighbors	Number of neighbors used for attribute estimation

◆ estimate_normals_covariances_omp() [1/2]

template<typename PointCloud >

void small_gicp::estimate_normals_covariances_omp	(	PointCloud &	cloud,
		int	num_neighbors = `20`,
		int	num_threads = `4`
	)

Estimate point normals and covariances with OpenMP.

Note: If a sufficient number of neighbor points for normal/covariance estimation (5 points) is not found, an invalid normal/covariance is set to the point (normal=zero vector, covariance=identity matrix).

Parameters

cloud	[in/out] Point cloud
num_neighbors	Number of neighbors used for attribute estimation
num_threads	Number of threads

◆ estimate_normals_covariances_omp() [2/2]

template<typename PointCloud , typename KdTree >

void small_gicp::estimate_normals_covariances_omp	(	PointCloud &	cloud,
		KdTree &	kdtree,
		int	num_neighbors = `20`,
		int	num_threads = `4`
	)

Estimate point normals and covariances with OpenMP.

Note: If a sufficient number of neighbor points for normal/covariance estimation (5 points) is not found, an invalid normal/covariance is set to the point (normal=zero vector, covariance=identity matrix).

Parameters

cloud	[in/out] Point cloud
kdtree	Nearest neighbor search
num_neighbors	Number of neighbors used for attribute estimation
num_threads	Number of threads

◆ estimate_normals_covariances_tbb() [1/2]

template<typename PointCloud >

void small_gicp::estimate_normals_covariances_tbb	(	PointCloud &	cloud,
		int	num_neighbors = `20`
	)

Estimate point normals and covariances with TBB.

Note: If a sufficient number of neighbor points for normal/covariance estimation (5 points) is not found, an invalid normal/covariance is set to the point (normal=zero vector, covariance=identity matrix).

Parameters

cloud	[in/out] Point cloud
num_neighbors	Number of neighbors used for attribute estimation

◆ estimate_normals_covariances_tbb() [2/2]

template<typename PointCloud , typename KdTree >

void small_gicp::estimate_normals_covariances_tbb	(	PointCloud &	cloud,
		KdTree &	kdtree,
		int	num_neighbors = `20`
	)

Estimate point normals and covariances with TBB.

Note: If a sufficient number of neighbor points for normal/covariance estimation (5 points) is not found, an invalid normal/covariance is set to the point (normal=zero vector, covariance=identity matrix).

Parameters

cloud	[in/out] Point cloud
kdtree	Nearest neighbor search
num_neighbors	Number of neighbors used for attribute estimation

◆ estimate_normals_omp() [1/2]

template<typename PointCloud >

void small_gicp::estimate_normals_omp	(	PointCloud &	cloud,
		int	num_neighbors = `20`,
		int	num_threads = `4`
	)

Estimate point normals with OpenMP.

Note: If a sufficient number of neighbor points for normal/covariance estimation (5 points) is not found, an invalid normal/covariance is set to the point (normal=zero vector, covariance=identity matrix).

Parameters

cloud	[in/out] Point cloud
num_neighbors	Number of neighbors used for attribute estimation
num_threads	Number of threads

◆ estimate_normals_omp() [2/2]

template<typename PointCloud , typename KdTree >

void small_gicp::estimate_normals_omp	(	PointCloud &	cloud,
		KdTree &	kdtree,
		int	num_neighbors = `20`,
		int	num_threads = `4`
	)

Estimate point normals with OpenMP.

Note: If a sufficient number of neighbor points for normal/covariance estimation (5 points) is not found, an invalid normal/covariance is set to the point (normal=zero vector, covariance=identity matrix).

Parameters

cloud	[in/out] Point cloud
kdtree	Nearest neighbor search
num_neighbors	Number of neighbors used for attribute estimation
num_threads	Number of threads

◆ estimate_normals_tbb() [1/2]

template<typename PointCloud >

void small_gicp::estimate_normals_tbb	(	PointCloud &	cloud,
		int	num_neighbors = `20`
	)

Estimate point normals with TBB.

Note: If a sufficient number of neighbor points for normal/covariance estimation (5 points) is not found, an invalid normal/covariance is set to the point (normal=zero vector, covariance=identity matrix).

Parameters

cloud	[in/out] Point cloud
num_neighbors	Number of neighbors used for attribute estimation

◆ estimate_normals_tbb() [2/2]

template<typename PointCloud , typename KdTree >

void small_gicp::estimate_normals_tbb	(	PointCloud &	cloud,
		KdTree &	kdtree,
		int	num_neighbors = `20`
	)

Estimate point normals with TBB.

Note: If a sufficient number of neighbor points for normal/covariance estimation (5 points) is not found, an invalid normal/covariance is set to the point (normal=zero vector, covariance=identity matrix).

Parameters

cloud	[in/out] Point cloud
kdtree	Nearest neighbor search
num_neighbors	Number of neighbors used for attribute estimation

◆ fast_floor()

Eigen::Array4i small_gicp::fast_floor ( const Eigen::Array4d & pt )

inline

Fast floor (https://stackoverflow.com/questions/824118/why-is-floor-so-slow).

Parameters

pt	Double vector

Returns: Floored int vector

◆ merge_sort_omp()

template<typename RandomAccessIterator , typename Compare >

void small_gicp::merge_sort_omp	(	RandomAccessIterator	first,
		RandomAccessIterator	last,
		const Compare &	comp,
		int	num_threads
	)

Merge sort with OpenMP parallelism.

Note: This tends to be slower than quick_sort_omp.

Parameters

first	First iterator
last	Last iterator
comp	Comparison function
num_threads	Number of threads

◆ merge_sort_omp_impl()

template<typename RandomAccessIterator , typename Compare >

void small_gicp::merge_sort_omp_impl	(	RandomAccessIterator	first,
		RandomAccessIterator	last,
		const Compare &	comp
	)

Implementation of merge sort with OpenMP parallelism. Do not call this directly. Use merge_sort_omp instead.

Parameters

first	First iterator
last	Last iterator
comp	Comparison function

◆ omp_get_thread_num()

int small_gicp::omp_get_thread_num ( )

inline

◆ preprocess_points() [1/2]

std::pair<PointCloud::Ptr, std::shared_ptr<KdTree<PointCloud> > > small_gicp::preprocess_points	(	const PointCloud &	points,
		double	downsampling_resolution,
		int	num_neighbors = `10`,
		int	num_threads = `4`
	)

Preprocess point cloud (downsampling, kdtree creation, and normal and covariance estimation).

Note: When num_threads >= 2, this function has minor run-by-run non-determinism due to the parallel downsampling.

See also: small_gicp::voxelgrid_sampling_omp, small_gicp::estimate_normals_covariances_omp

Parameters

points	Input points
downsampling_resolution	Downsample resolution
num_neighbors	Number of neighbors for normal/covariance estimation
num_threads	Number of threads

◆ preprocess_points() [2/2]

template<typename T , int D>

std::pair<PointCloud::Ptr, std::shared_ptr<KdTree<PointCloud> > > small_gicp::preprocess_points	(	const std::vector< Eigen::Matrix< T, D, 1 >> &	points,
		double	downsampling_resolution,
		int	num_neighbors = `10`,
		int	num_threads = `4`
	)

Preprocess point cloud (downsampling, kdtree creation, and normal and covariance estimation)

Note: This function only accepts Eigen::Vector(3|4)(f|d) as input; When num_threads >= 2, this function has minor run-by-run non-determinism due to the parallel downsampling.

See also: small_gicp::voxelgrid_sampling_omp, small_gicp::estimate_normals_covariances_omp

◆ quick_sort_omp()

template<typename RandomAccessIterator , typename Compare >

void small_gicp::quick_sort_omp	(	RandomAccessIterator	first,
		RandomAccessIterator	last,
		const Compare &	comp,
		int	num_threads
	)

Quick sort with OpenMP parallelism.

Parameters

first	First iterator
last	Last iterator
comp	Comparison function
num_threads	Number of threads

◆ quick_sort_omp_impl()

template<typename RandomAccessIterator , typename Compare >

void small_gicp::quick_sort_omp_impl	(	RandomAccessIterator	first,
		RandomAccessIterator	last,
		const Compare &	comp
	)

Implementation of quick sort with OpenMP parallelism. Do not call this directly. Use quick_sort_omp instead.

Parameters

first	First iterator
last	Last iterator
comp	Comparison function

◆ radix_sort_tbb() [1/2]

template<typename T , typename KeyFunc , int bits = 4, int tile_size = 256>

void small_gicp::radix_sort_tbb	(	T *	first_,
		T *	last_,
		const KeyFunc &	key_
	)

Radix sort with TBB parallelization.

Template Parameters

T	Data type (must be unsigned integral type)
KeyFunc	Key function
bits	Number of bits per step
tile_size	Tile size

Parameters

first_	[in/out] First iterator
last_	[in/out] Last iterator
key_	Key function (T => uint)

◆ radix_sort_tbb() [2/2]

template<typename T , typename KeyFunc , int bits = 8, int tile_size = 256>

void small_gicp::radix_sort_tbb	(	T *	first_,
		T *	last_,
		const KeyFunc &	key_,
		RadixSortBuffers< T > &	buffers
	)

Radix sort with TBB parallelization.

Note: This function outperforms tbb::parallel_sort only in case with many elements and threads. For usual data size and number of threads, use tbb::parallel_sort.

Template Parameters

T	Data type (must be unsigned integral type)
KeyFunc	Key function
bits	Number of bits per step
tile_size	Tile size

Parameters

first_	[in/out] First iterator
last_	[in/out] Last iterator
key_	Key function (T => uint)
buffers	Temporal buffers

◆ random_sampling()

template<typename InputPointCloud , typename OutputPointCloud = InputPointCloud, typename RNG = std::mt19937>

std::shared_ptr<OutputPointCloud> small_gicp::random_sampling	(	const InputPointCloud &	points,
		size_t	num_samples,
		RNG &	rng
	)

Random downsampling.

Parameters

points	Input points
num_samples	Number of samples to be drawn

Returns: Downsampled points

◆ se3_exp()

Eigen::Isometry3d small_gicp::se3_exp ( const Eigen::Matrix< double, 6, 1 > & a )

inline

SE3 expmap (Rotation-first).

Parameters

a	Twist vector [rx, ry, rz, tx, ty, tz]

Returns: SE3 matrix

Note: That is an accurate expansion!

◆ skew()

Eigen::Matrix3d small_gicp::skew ( const Eigen::Vector3d & x )

inline

Create skew symmetric matrix.

Parameters

x	Rotation vector

Returns: Skew symmetric matrix

◆ so3_exp()

Eigen::Quaterniond small_gicp::so3_exp ( const Eigen::Vector3d & omega )

inline

SO3 expmap.

Parameters

omega [rx, ry, rz]

Returns: Quaternion

◆ voxelgrid_sampling()

template<typename InputPointCloud , typename OutputPointCloud = InputPointCloud>

std::shared_ptr<OutputPointCloud> small_gicp::voxelgrid_sampling	(	const InputPointCloud &	points,
		double	leaf_size
	)

Voxelgrid downsampling. This function computes exact average of points in each voxel, and each voxel can contain arbitrary number of points.

Note: Discretized voxel coords must be in 21bit range [-1048576, 1048575]. For example, if the downsampling resolution is 0.01 m, point coordinates must be in [-10485.76, 10485.75] m. Points outside the valid range will be ignored.

Parameters

points	Input points
leaf_size	Downsampling resolution

Returns: Downsampled points

◆ voxelgrid_sampling_omp()

template<typename InputPointCloud , typename OutputPointCloud = InputPointCloud>

std::shared_ptr<OutputPointCloud> small_gicp::voxelgrid_sampling_omp	(	const InputPointCloud &	points,
		double	leaf_size,
		int	num_threads = `4`
	)

Voxel grid downsampling with OpenMP backend.

Note: This function has minor run-by-run non-deterministic behavior due to parallel data collection that results in a deviation of the number of points in the downsampling results (up to 10% increase from the single-thread version).; Discretized voxel coords must be in 21bit range [-1048576, 1048575]. For example, if the downsampling resolution is 0.01 m, point coordinates must be in [-10485.76, 10485.75] m. Points outside the valid range will be ignored.

Parameters

points	Input points
leaf_size	Downsampling resolution

Returns: Downsampled points

◆ voxelgrid_sampling_tbb()

template<typename InputPointCloud , typename OutputPointCloud = InputPointCloud>

std::shared_ptr<OutputPointCloud> small_gicp::voxelgrid_sampling_tbb	(	const InputPointCloud &	points,
		double	leaf_size
	)

Voxel grid downsampling with TBB backend.

Note: This function has minor run-by-run non-deterministic behavior due to parallel data collection that results in a deviation of the number of points in the downsampling results (up to 10% increase from the single-thread version).; Discretized voxel coords must be in 21bit range [-1048576, 1048575]. For example, if the downsampling resolution is 0.01 m, point coordinates must be in [-10485.76, 10485.75] m. Points outside the valid range will be ignored.

Parameters

points	Input points
leaf_size	Downsampling resolution

Returns: Downsampled points

Namespaces

Classes

Typedefs

Functions

Typedef Documentation

◆ FlatContainerCov

◆ FlatContainerNormal

◆ FlatContainerNormalCov

◆ FlatContainerPoints

◆ GaussianVoxelMap

◆ NodeIndexType

Function Documentation

◆ align() [1/3]

◆ align() [2/3]

◆ align() [3/3]

◆ create_gaussian_voxelmap()

◆ estimate_covariances() [1/2]

◆ estimate_covariances() [2/2]

◆ estimate_covariances_omp() [1/2]

◆ estimate_covariances_omp() [2/2]

◆ estimate_covariances_tbb() [1/2]

◆ estimate_covariances_tbb() [2/2]

◆ estimate_local_features() [1/3]

◆ estimate_local_features() [2/3]

◆ estimate_local_features() [3/3]

◆ estimate_local_features_omp() [1/2]

◆ estimate_local_features_omp() [2/2]

◆ estimate_local_features_tbb() [1/2]

◆ estimate_local_features_tbb() [2/2]

◆ estimate_normals() [1/2]

◆ estimate_normals() [2/2]

◆ estimate_normals_covariances() [1/2]

◆ estimate_normals_covariances() [2/2]

◆ estimate_normals_covariances_omp() [1/2]

◆ estimate_normals_covariances_omp() [2/2]

◆ estimate_normals_covariances_tbb() [1/2]

◆ estimate_normals_covariances_tbb() [2/2]

◆ estimate_normals_omp() [1/2]

◆ estimate_normals_omp() [2/2]

◆ estimate_normals_tbb() [1/2]

◆ estimate_normals_tbb() [2/2]

◆ fast_floor()

◆ merge_sort_omp()

◆ merge_sort_omp_impl()

◆ omp_get_thread_num()

◆ preprocess_points() [1/2]

◆ preprocess_points() [2/2]

◆ quick_sort_omp()

◆ quick_sort_omp_impl()

◆ radix_sort_tbb() [1/2]

◆ radix_sort_tbb() [2/2]

◆ random_sampling()

◆ se3_exp()

◆ skew()

◆ so3_exp()

◆ voxelgrid_sampling()

◆ voxelgrid_sampling_omp()

◆ voxelgrid_sampling_tbb()