#include <kdtree.hpp>

Collaboration diagram for small_gicp::UnsafeKdTree< PointCloud, Projection_ >:

Public Types
using	Projection = Projection_

using	Node = KdTreeNode< Projection >

Public Member Functions
template<typename Builder = KdTreeBuilder>
	UnsafeKdTree (const PointCloud &points, const Builder &builder=KdTreeBuilder())
	Constructor. More...

size_t	nearest_neighbor_search (const Eigen::Vector4d &query, size_t k_indices, double k_sq_dists, const KnnSetting &setting=KnnSetting()) const
	Find the nearest neighbor. More...

size_t	knn_search (const Eigen::Vector4d &query, int k, size_t k_indices, double k_sq_dists, const KnnSetting &setting=KnnSetting()) const
	Find k-nearest neighbors. This method uses dynamic memory allocation. More...

template<int N>
size_t	knn_search (const Eigen::Vector4d &query, size_t k_indices, double k_sq_dists, const KnnSetting &setting=KnnSetting()) const
	Find k-nearest neighbors. This method uses fixed and static memory allocation. Might be faster for small k. More...

Public Attributes
const PointCloud &	points
	Input points. More...

std::vector< size_t >	indices
	Point indices refered by nodes. More...

NodeIndexType	root
	Root node index (should be zero) More...

std::vector< Node >	nodes
	Kd-tree nodes. More...

Detailed Description

template<typename PointCloud, typename Projection_ = AxisAlignedProjection>
struct small_gicp::UnsafeKdTree< PointCloud, Projection_ >

"Unsafe" KdTree.

Note: This class does not hold the ownership of the input points. You must keep the input points along with this class.

Member Typedef Documentation

◆ Node

template<typename PointCloud , typename Projection_ = AxisAlignedProjection>

using small_gicp::UnsafeKdTree< PointCloud, Projection_ >::Node = KdTreeNode<Projection>

◆ Projection

template<typename PointCloud , typename Projection_ = AxisAlignedProjection>

using small_gicp::UnsafeKdTree< PointCloud, Projection_ >::Projection = Projection_

Constructor & Destructor Documentation

◆ UnsafeKdTree()

template<typename PointCloud , typename Projection_ = AxisAlignedProjection>

template<typename Builder = KdTreeBuilder>

small_gicp::UnsafeKdTree< PointCloud, Projection_ >::UnsafeKdTree	(	const PointCloud &	points,
		const Builder &	builder = `KdTreeBuilder()`
	)

inlineexplicit

Constructor.

Parameters

points	Point cloud
builder	Kd-tree builder

Member Function Documentation

◆ knn_search() [1/2]

template<typename PointCloud , typename Projection_ = AxisAlignedProjection>

size_t small_gicp::UnsafeKdTree< PointCloud, Projection_ >::knn_search	(	const Eigen::Vector4d &	query,
		int	k,
		size_t *	k_indices,
		double *	k_sq_dists,
		const KnnSetting &	setting = `KnnSetting()`
	)		const

inline

Find k-nearest neighbors. This method uses dynamic memory allocation.

Parameters

query	Query point
k	Number of neighbors
k_indices	Indices of neighbors
k_sq_dists	Squared distances to neighbors (sorted in ascending order)
setting	KNN search setting

Returns: Number of found neighbors

◆ knn_search() [2/2]

template<typename PointCloud , typename Projection_ = AxisAlignedProjection>

template<int N>

size_t small_gicp::UnsafeKdTree< PointCloud, Projection_ >::knn_search	(	const Eigen::Vector4d &	query,
		size_t *	k_indices,
		double *	k_sq_dists,
		const KnnSetting &	setting = `KnnSetting()`
	)		const

inline

Find k-nearest neighbors. This method uses fixed and static memory allocation. Might be faster for small k.

Parameters

query	Query point
k_indices	Indices of neighbors
k_sq_dists	Squared distances to neighbors (sorted in ascending order)
setting	KNN search setting

Returns: Number of found neighbors

◆ nearest_neighbor_search()

template<typename PointCloud , typename Projection_ = AxisAlignedProjection>

size_t small_gicp::UnsafeKdTree< PointCloud, Projection_ >::nearest_neighbor_search	(	const Eigen::Vector4d &	query,
		size_t *	k_indices,
		double *	k_sq_dists,
		const KnnSetting &	setting = `KnnSetting()`
	)		const

inline

Find the nearest neighbor.

Parameters

query	Query point
k_indices	Index of the nearest neighbor (uninitialized if not found)
k_sq_dists	Squared distance to the nearest neighbor (uninitialized if not found)
setting	KNN search setting

Returns: Number of found neighbors (0 or 1)

Member Data Documentation

◆ indices

template<typename PointCloud , typename Projection_ = AxisAlignedProjection>

std::vector<size_t> small_gicp::UnsafeKdTree< PointCloud, Projection_ >::indices

Point indices refered by nodes.

◆ nodes

template<typename PointCloud , typename Projection_ = AxisAlignedProjection>

std::vector<Node> small_gicp::UnsafeKdTree< PointCloud, Projection_ >::nodes

Kd-tree nodes.

◆ points

template<typename PointCloud , typename Projection_ = AxisAlignedProjection>

const PointCloud& small_gicp::UnsafeKdTree< PointCloud, Projection_ >::points

Input points.

◆ root

template<typename PointCloud , typename Projection_ = AxisAlignedProjection>

NodeIndexType small_gicp::UnsafeKdTree< PointCloud, Projection_ >::root

Root node index (should be zero)

The documentation for this struct was generated from the following file:

/home/runner/work/small_gicp/small_gicp/include/small_gicp/ann/kdtree.hpp

Public Types

Public Member Functions

Public Attributes

Detailed Description

template<typename PointCloud, typename Projection_ = AxisAlignedProjection> struct small_gicp::UnsafeKdTree< PointCloud, Projection_ >

Member Typedef Documentation

◆ Node

◆ Projection

Constructor & Destructor Documentation

◆ UnsafeKdTree()

Member Function Documentation

◆ knn_search() [1/2]

◆ knn_search() [2/2]

◆ nearest_neighbor_search()

Member Data Documentation

◆ indices

◆ nodes

◆ points

◆ root

template<typename PointCloud, typename Projection_ = AxisAlignedProjection>
struct small_gicp::UnsafeKdTree< PointCloud, Projection_ >