search::KdTree is a wrapper class which inherits the pcl::KdTree class for performing search functions using KdTree structure. More...

#include <pcl/search/kdtree.h>

Inheritance diagram for pcl::search::KdTree< PointT, Tree >:

Public Types
using	PointCloud = typename Search<PointT>::PointCloud
using	PointCloudConstPtr = typename Search<PointT>::PointCloudConstPtr
using	Ptr = shared_ptr<KdTree<PointT, Tree> >
using	ConstPtr = shared_ptr<const KdTree<PointT, Tree> >
using	KdTreePtr = typename Tree::Ptr
using	KdTreeConstPtr = typename Tree::ConstPtr
using	PointRepresentationConstPtr = typename PointRepresentation<PointT>::ConstPtr
Public Types inherited from pcl::search::Search< PointT >
using	PointCloud = pcl::PointCloud<PointT>
using	PointCloudPtr = typename PointCloud::Ptr
using	PointCloudConstPtr = typename PointCloud::ConstPtr
using	Ptr = shared_ptr<pcl::search::Search<PointT> >
using	ConstPtr = shared_ptr<const pcl::search::Search<PointT> >
using	IndicesPtr = pcl::IndicesPtr
using	IndicesConstPtr = pcl::IndicesConstPtr

Public Member Functions
	KdTree (bool sorted=true)
	Constructor for KdTree.
	~KdTree () override=default
	Destructor for KdTree.
void	setPointRepresentation (const PointRepresentationConstPtr &point_representation)
	Provide a pointer to the point representation to use to convert points into k-D vectors.
PointRepresentationConstPtr	getPointRepresentation () const
	Get a pointer to the point representation used when converting points into k-D vectors.
void	setSortedResults (bool sorted_results) override
	Sets whether the results have to be sorted or not.
void	setEpsilon (float eps)
	Set the search epsilon precision (error bound) for nearest neighbors searches.
float	getEpsilon () const
	Get the search epsilon precision (error bound) for nearest neighbors searches.
bool	setInputCloud (const PointCloudConstPtr &cloud, const IndicesConstPtr &indices=IndicesConstPtr()) override
	Provide a pointer to the input dataset.
int	nearestKSearch (const PointT &point, int k, Indices &k_indices, std::vector< float > &k_sqr_distances) const override
	Search for the k-nearest neighbors for the given query point.
int	radiusSearch (const PointT &point, double radius, Indices &k_indices, std::vector< float > &k_sqr_distances, unsigned int max_nn=0) const override
	Search for all the nearest neighbors of the query point in a given radius.
Public Member Functions inherited from pcl::search::Search< PointT >
	Search (const std::string &name="", bool sorted=false)
	Constructor.
virtual	~Search ()=default
	Destructor.
virtual const std::string &	getName () const
	Returns the search method name.
virtual bool	getSortedResults ()
	Gets whether the results should be sorted (ascending in the distance) or not Otherwise the results may be returned in any order.
virtual PointCloudConstPtr	getInputCloud () const
	Get a pointer to the input point cloud dataset.
virtual IndicesConstPtr	getIndices () const
	Get a pointer to the vector of indices used.
template<typename PointTDiff>
int	nearestKSearchT (const PointTDiff &point, int k, Indices &k_indices, std::vector< float > &k_sqr_distances) const
	Search for k-nearest neighbors for the given query point.
virtual int	nearestKSearch (const PointCloud &cloud, index_t index, int k, Indices &k_indices, std::vector< float > &k_sqr_distances) const
	Search for k-nearest neighbors for the given query point.
virtual int	nearestKSearch (index_t index, int k, Indices &k_indices, std::vector< float > &k_sqr_distances) const
	Search for k-nearest neighbors for the given query point (zero-copy).
virtual void	nearestKSearch (const PointCloud &cloud, const Indices &indices, int k, std::vector< Indices > &k_indices, std::vector< std::vector< float > > &k_sqr_distances) const
	Search for the k-nearest neighbors for the given query point.
template<typename PointTDiff>
void	nearestKSearchT (const pcl::PointCloud< PointTDiff > &cloud, const Indices &indices, int k, std::vector< Indices > &k_indices, std::vector< std::vector< float > > &k_sqr_distances) const
	Search for the k-nearest neighbors for the given query point.
template<typename PointTDiff>
int	radiusSearchT (const PointTDiff &point, double radius, Indices &k_indices, std::vector< float > &k_sqr_distances, unsigned int max_nn=0) const
	Search for all the nearest neighbors of the query point in a given radius.
virtual int	radiusSearch (const PointCloud &cloud, index_t index, double radius, Indices &k_indices, std::vector< float > &k_sqr_distances, unsigned int max_nn=0) const
	Search for all the nearest neighbors of the query point in a given radius.
virtual int	radiusSearch (index_t index, double radius, Indices &k_indices, std::vector< float > &k_sqr_distances, unsigned int max_nn=0) const
	Search for all the nearest neighbors of the query point in a given radius (zero-copy).
virtual void	radiusSearch (const PointCloud &cloud, const Indices &indices, double radius, std::vector< Indices > &k_indices, std::vector< std::vector< float > > &k_sqr_distances, unsigned int max_nn=0) const
	Search for all the nearest neighbors of the query point in a given radius.
template<typename PointTDiff>
void	radiusSearchT (const pcl::PointCloud< PointTDiff > &cloud, const Indices &indices, double radius, std::vector< Indices > &k_indices, std::vector< std::vector< float > > &k_sqr_distances, unsigned int max_nn=0) const
	Search for all the nearest neighbors of the query points in a given radius.

Protected Member Functions
	KdTree (const std::string &name, bool sorted)
	This leaves the internal tree_ object uninitialized!
Protected Member Functions inherited from pcl::search::Search< PointT >
void	sortResults (Indices &indices, std::vector< float > &distances) const

Protected Attributes
KdTreePtr	tree_
	A pointer to the internal KdTree object.
Protected Attributes inherited from pcl::search::Search< PointT >
PointCloudConstPtr	input_
IndicesConstPtr	indices_
bool	sorted_results_
std::string	name_

Detailed Description

template<typename PointT, class Tree = pcl::KdTreeFLANN<PointT>>
class pcl::search::KdTree< PointT, Tree >

search::KdTree is a wrapper class which inherits the pcl::KdTree class for performing search functions using KdTree structure.

KdTree is a generic type of 3D spatial locator using kD-tree structures. The class is making use of the FLANN (Fast Library for Approximate Nearest Neighbor) project by Marius Muja and David Lowe.

Author: Radu B. Rusu

Definition at line 61 of file kdtree.h.

Member Typedef Documentation

◆ ConstPtr

template<typename PointT, class Tree = pcl::KdTreeFLANN<PointT>>

using pcl::search::KdTree< PointT, Tree >::ConstPtr = shared_ptr<const KdTree<PointT, Tree> >

Definition at line 76 of file kdtree.h.

◆ KdTreeConstPtr

template<typename PointT, class Tree = pcl::KdTreeFLANN<PointT>>

using pcl::search::KdTree< PointT, Tree >::KdTreeConstPtr = typename Tree::ConstPtr

Definition at line 79 of file kdtree.h.

◆ KdTreePtr

template<typename PointT, class Tree = pcl::KdTreeFLANN<PointT>>

using pcl::search::KdTree< PointT, Tree >::KdTreePtr = typename Tree::Ptr

Definition at line 78 of file kdtree.h.

◆ PointCloud

template<typename PointT, class Tree = pcl::KdTreeFLANN<PointT>>

using pcl::search::KdTree< PointT, Tree >::PointCloud = typename Search<PointT>::PointCloud

Definition at line 64 of file kdtree.h.

◆ PointCloudConstPtr

template<typename PointT, class Tree = pcl::KdTreeFLANN<PointT>>

using pcl::search::KdTree< PointT, Tree >::PointCloudConstPtr = typename Search<PointT>::PointCloudConstPtr

Definition at line 65 of file kdtree.h.

◆ PointRepresentationConstPtr

template<typename PointT, class Tree = pcl::KdTreeFLANN<PointT>>

using pcl::search::KdTree< PointT, Tree >::PointRepresentationConstPtr = typename PointRepresentation<PointT>::ConstPtr

Definition at line 80 of file kdtree.h.

◆ Ptr

template<typename PointT, class Tree = pcl::KdTreeFLANN<PointT>>

using pcl::search::KdTree< PointT, Tree >::Ptr = shared_ptr<KdTree<PointT, Tree> >

Definition at line 75 of file kdtree.h.

Constructor & Destructor Documentation

◆ KdTree() [1/2]

template<typename PointT, class Tree>

pcl::search::KdTree< PointT, Tree >::KdTree ( bool sorted = true )

Constructor for KdTree.

Parameters

[in] sorted set to true if the nearest neighbor search results need to be sorted in ascending order based on their distance to the query point

Definition at line 45 of file kdtree.hpp.

References pcl::search::Search< PointT >::Search(), and tree_.

Referenced by pcl::search::KdTreeNanoflann< PointT, Dim, Distance, L2_Adaptor, L2_Simple_Adaptor, Tree >::KdTreeNanoflann().

◆ ~KdTree()

template<typename PointT, class Tree = pcl::KdTreeFLANN<PointT>>

pcl::search::KdTree< PointT, Tree >::~KdTree ( )

overridedefault

Destructor for KdTree.

◆ KdTree() [2/2]

template<typename PointT, class Tree>

pcl::search::KdTree< PointT, Tree >::KdTree	(	const std::string &	name,
		bool	sorted )

protected

This leaves the internal tree_ object uninitialized!

Definition at line 52 of file kdtree.hpp.

References pcl::search::Search< PointT >::Search().

Member Function Documentation

◆ getEpsilon()

template<typename PointT, class Tree = pcl::KdTreeFLANN<PointT>>

float pcl::search::KdTree< PointT, Tree >::getEpsilon ( ) const

inline

Get the search epsilon precision (error bound) for nearest neighbors searches.

Definition at line 122 of file kdtree.h.

◆ getPointRepresentation()

template<typename PointT, class Tree = pcl::KdTreeFLANN<PointT>>

PointRepresentationConstPtr pcl::search::KdTree< PointT, Tree >::getPointRepresentation ( ) const

inline

Get a pointer to the point representation used when converting points into k-D vectors.

Definition at line 103 of file kdtree.h.

◆ nearestKSearch()

template<typename PointT, class Tree>

int pcl::search::KdTree< PointT, Tree >::nearestKSearch	(	const PointT &	point,
		int	k,
		Indices &	k_indices,
		std::vector< float > &	k_sqr_distances ) const

overridevirtual

Search for the k-nearest neighbors for the given query point.

Parameters

[in]	point	the given query point
[in]	k	the number of neighbors to search for
[out]	k_indices	the resultant indices of the neighboring points (must be resized to k a priori!)
[out]	k_sqr_distances	the resultant squared distances to the neighboring points (must be resized to k a priori!)

Returns: number of neighbors found

Implements pcl::search::Search< PointT >.

Reimplemented in pcl::search::KdTreeNanoflann< PointT, Dim, Distance, L2_Adaptor, L2_Simple_Adaptor, Tree >.

Definition at line 100 of file kdtree.hpp.

References tree_.

Referenced by pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::computeCovariances(), pcl::registration::FPCSInitialAlignment< PointSource, PointTarget, pcl::Normal, float >::FPCSInitialAlignment(), and pcl::getMeanPointDensity().

◆ radiusSearch()

template<typename PointT, class Tree>

int pcl::search::KdTree< PointT, Tree >::radiusSearch	(	const PointT &	point,
		double	radius,
		Indices &	k_indices,
		std::vector< float > &	k_sqr_distances,
		unsigned int	max_nn = 0 ) const

overridevirtual

Search for all the nearest neighbors of the query point in a given radius.

Parameters

[in]	point	the given query point
[in]	radius	the radius of the sphere bounding all of p_q's neighbors
[out]	k_indices	the resultant indices of the neighboring points
[out]	k_sqr_distances	the resultant squared distances to the neighboring points
[in]	max_nn	if given, bounds the maximum returned neighbors to this value. If max_nn is set to 0 or to a number higher than the number of points in the input cloud, all neighbors in radius will be returned.

Returns: number of neighbors found in radius

Implements pcl::search::Search< PointT >.

Reimplemented in pcl::search::KdTreeNanoflann< PointT, Dim, Distance, L2_Adaptor, L2_Simple_Adaptor, Tree >.

Definition at line 109 of file kdtree.hpp.

◆ setEpsilon()

template<typename PointT, class Tree>

void pcl::search::KdTree< PointT, Tree >::setEpsilon ( float eps )

Set the search epsilon precision (error bound) for nearest neighbors searches.

Parameters

[in] eps precision (error bound) for nearest neighbors searches

Definition at line 78 of file kdtree.hpp.

References tree_.

◆ setInputCloud()

template<typename PointT, class Tree>

bool pcl::search::KdTree< PointT, Tree >::setInputCloud	(	const PointCloudConstPtr &	cloud,
		const IndicesConstPtr &	indices = IndicesConstPtr () )

overridevirtual

Provide a pointer to the input dataset.

Parameters

[in]	cloud	the const boost shared pointer to a PointCloud message
[in]	indices	the point indices subset that is to be used from cloud

Reimplemented from pcl::search::Search< PointT >.

Reimplemented in pcl::search::KdTreeNanoflann< PointT, Dim, Distance, L2_Adaptor, L2_Simple_Adaptor, Tree >.

Definition at line 88 of file kdtree.hpp.

Referenced by pcl::people::GroundBasedPeopleDetectionApp< PointT >::compute(), pcl::getMeanPointDensity(), and pcl::getMeanPointDensity().

◆ setPointRepresentation()

template<typename PointT, class Tree>

void pcl::search::KdTree< PointT, Tree >::setPointRepresentation ( const PointRepresentationConstPtr & point_representation )

Provide a pointer to the point representation to use to convert points into k-D vectors.

Parameters

[in] point_representation the const boost shared pointer to a PointRepresentation

Definition at line 59 of file kdtree.hpp.

References tree_.

◆ setSortedResults()

template<typename PointT, class Tree>

void pcl::search::KdTree< PointT, Tree >::setSortedResults ( bool sorted_results )

overridevirtual

Sets whether the results have to be sorted or not.

Parameters

[in] sorted_results set to true if the radius search results should be sorted

Reimplemented from pcl::search::Search< PointT >.

Reimplemented in pcl::search::KdTreeNanoflann< PointT, Dim, Distance, L2_Adaptor, L2_Simple_Adaptor, Tree >.

Definition at line 70 of file kdtree.hpp.

References pcl::search::Search< PointT >::sorted_results_, and tree_.

Member Data Documentation

◆ tree_

template<typename PointT, class Tree = pcl::KdTreeFLANN<PointT>>

KdTreePtr pcl::search::KdTree< PointT, Tree >::tree_

protected

A pointer to the internal KdTree object.

Definition at line 167 of file kdtree.h.

Referenced by KdTree(), nearestKSearch(), setEpsilon(), setPointRepresentation(), and setSortedResults().

The documentation for this class was generated from the following files:

pcl/search/kdtree.h
pcl/search/impl/kdtree.hpp

Public Types

Public Member Functions

Protected Member Functions

Protected Attributes

Detailed Description

Member Typedef Documentation

◆ ConstPtr

◆ KdTreeConstPtr

◆ KdTreePtr

◆ PointCloud

◆ PointCloudConstPtr

◆ PointRepresentationConstPtr

◆ Ptr

Constructor & Destructor Documentation

◆ KdTree() [1/2]

◆ ~KdTree()

◆ KdTree() [2/2]

Member Function Documentation

◆ getEpsilon()

◆ getPointRepresentation()

◆ nearestKSearch()

◆ radiusSearch()

◆ setEpsilon()

◆ setInputCloud()

◆ setPointRepresentation()

◆ setSortedResults()

Member Data Documentation

◆ tree_