KdTreeFLANN is a generic type of 3D spatial locator using kD-tree structures. More...

#include <pcl/kdtree/kdtree_flann.h>

Inheritance diagram for pcl::KdTreeFLANN< PointT, Dist >:

Collaboration diagram for pcl::KdTreeFLANN< PointT, Dist >:

Public Types
using	PointCloud = typename KdTree< PointT >::PointCloud

using	PointCloudConstPtr = typename KdTree< PointT >::PointCloudConstPtr

using	IndicesPtr = shared_ptr< Indices >

using	IndicesConstPtr = shared_ptr< const Indices >

using	FLANNIndex = ::flann::Index< Dist >

using	Ptr = shared_ptr< KdTreeFLANN< PointT, Dist > >

using	ConstPtr = shared_ptr< const KdTreeFLANN< PointT, Dist > >

Public Types inherited from pcl::KdTree< PointT >
using	IndicesPtr = shared_ptr< Indices >

using	IndicesConstPtr = shared_ptr< const Indices >

using	PointCloud = pcl::PointCloud< PointT >

using	PointCloudPtr = typename PointCloud::Ptr

using	PointCloudConstPtr = typename PointCloud::ConstPtr

using	PointRepresentation = pcl::PointRepresentation< PointT >

using	PointRepresentationConstPtr = typename PointRepresentation::ConstPtr

using	Ptr = shared_ptr< KdTree< PointT > >

using	ConstPtr = shared_ptr< const KdTree< PointT > >

Public Member Functions
	KdTreeFLANN (bool sorted=true)
	Default Constructor for KdTreeFLANN. More...

	KdTreeFLANN (const KdTreeFLANN< PointT, Dist > &k)
	Copy constructor. More...

KdTreeFLANN< PointT, Dist > &	operator= (const KdTreeFLANN< PointT, Dist > &k)
	Copy operator. More...

void	setEpsilon (float eps) override
	Set the search epsilon precision (error bound) for nearest neighbors searches. More...

void	setSortedResults (bool sorted)

Ptr	makeShared ()

	~KdTreeFLANN () override
	Destructor for KdTreeFLANN. More...

void	setInputCloud (const PointCloudConstPtr &cloud, const IndicesConstPtr &indices=IndicesConstPtr()) override
	Provide a pointer to the input dataset. More...

int	nearestKSearch (const PointT &point, unsigned int k, Indices &k_indices, std::vector< float > &k_sqr_distances) const override
	Search for k-nearest neighbors for the given query point. More...

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. More...

Public Member Functions inherited from pcl::KdTree< PointT >
	KdTree (bool sorted=true)
	Empty constructor for KdTree. More...

IndicesConstPtr	getIndices () const
	Get a pointer to the vector of indices used. More...

PointCloudConstPtr	getInputCloud () const
	Get a pointer to the input point cloud dataset. More...

void	setPointRepresentation (const PointRepresentationConstPtr &point_representation)
	Provide a pointer to the point representation to use to convert points into k-D vectors. More...

PointRepresentationConstPtr	getPointRepresentation () const
	Get a pointer to the point representation used when converting points into k-D vectors. More...

virtual	~KdTree ()=default
	Destructor for KdTree. More...

virtual int	nearestKSearch (const PointCloud &cloud, int index, unsigned int k, Indices &k_indices, std::vector< float > &k_sqr_distances) const
	Search for k-nearest neighbors for the given query point. More...

template<typename PointTDiff >
int	nearestKSearchT (const PointTDiff &point, unsigned int k, Indices &k_indices, std::vector< float > &k_sqr_distances) const
	Search for k-nearest neighbors for the given query point. More...

virtual int	nearestKSearch (int index, unsigned int k, Indices &k_indices, std::vector< float > &k_sqr_distances) const
	Search for k-nearest neighbors for the given query point (zero-copy). More...

virtual int	radiusSearch (const PointCloud &cloud, int 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. More...

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. More...

virtual int	radiusSearch (int 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). More...

float	getEpsilon () const
	Get the search epsilon precision (error bound) for nearest neighbors searches. More...

void	setMinPts (int min_pts)
	Minimum allowed number of k nearest neighbors points that a viable result must contain. More...

int	getMinPts () const
	Get the minimum allowed number of k nearest neighbors points that a viable result must contain. More...

bool	getSortedResults () const
	Gets whether the results should be sorted (ascending in the distance) or not Otherwise the results may be returned in any order. More...

Additional Inherited Members
Protected Attributes inherited from pcl::KdTree< PointT >
PointCloudConstPtr	input_
	The input point cloud dataset containing the points we need to use. More...

IndicesConstPtr	indices_
	A pointer to the vector of point indices to use. More...

float	epsilon_ {0.0f}
	Epsilon precision (error bound) for nearest neighbors searches. More...

int	min_pts_ {1}
	Minimum allowed number of k nearest neighbors points that a viable result must contain. More...

bool	sorted_
	Return the radius search neighbours sorted. More...

PointRepresentationConstPtr	point_representation_
	For converting different point structures into k-dimensional vectors for nearest-neighbor search. More...

Detailed Description

template<typename PointT, typename Dist = ::flann::L2_Simple<float>>
class pcl::KdTreeFLANN< PointT, Dist >

KdTreeFLANN 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, Marius Muja

Definition at line 132 of file kdtree_flann.h.

Member Typedef Documentation

◆ ConstPtr

template<typename PointT , typename Dist = ::flann::L2_Simple<float>>

using pcl::KdTreeFLANN< PointT, Dist >::ConstPtr = shared_ptr<const KdTreeFLANN<PointT, Dist> >

Definition at line 152 of file kdtree_flann.h.

◆ FLANNIndex

template<typename PointT , typename Dist = ::flann::L2_Simple<float>>

using pcl::KdTreeFLANN< PointT, Dist >::FLANNIndex = ::flann::Index<Dist>

Definition at line 148 of file kdtree_flann.h.

◆ IndicesConstPtr

template<typename PointT , typename Dist = ::flann::L2_Simple<float>>

using pcl::KdTreeFLANN< PointT, Dist >::IndicesConstPtr = shared_ptr<const Indices>

Definition at line 146 of file kdtree_flann.h.

◆ IndicesPtr

template<typename PointT , typename Dist = ::flann::L2_Simple<float>>

using pcl::KdTreeFLANN< PointT, Dist >::IndicesPtr = shared_ptr<Indices>

Definition at line 145 of file kdtree_flann.h.

◆ PointCloud

template<typename PointT , typename Dist = ::flann::L2_Simple<float>>

using pcl::KdTreeFLANN< PointT, Dist >::PointCloud = typename KdTree<PointT>::PointCloud

Definition at line 142 of file kdtree_flann.h.

◆ PointCloudConstPtr

template<typename PointT , typename Dist = ::flann::L2_Simple<float>>

using pcl::KdTreeFLANN< PointT, Dist >::PointCloudConstPtr = typename KdTree<PointT>::PointCloudConstPtr

Definition at line 143 of file kdtree_flann.h.

◆ Ptr

template<typename PointT , typename Dist = ::flann::L2_Simple<float>>

using pcl::KdTreeFLANN< PointT, Dist >::Ptr = shared_ptr<KdTreeFLANN<PointT, Dist> >

Definition at line 151 of file kdtree_flann.h.

Constructor & Destructor Documentation

◆ KdTreeFLANN() [1/2]

template<typename PointT , typename Dist >

pcl::KdTreeFLANN< PointT, Dist >::KdTreeFLANN ( bool sorted = true )

Default Constructor for KdTreeFLANN.

Parameters

[in] sorted set to true if the application that the tree will be used for requires sorted nearest neighbor indices (default). False otherwise.

By setting sorted to false, the radiusSearch operations will be faster.

Definition at line 49 of file kdtree_flann.hpp.

◆ KdTreeFLANN() [2/2]

template<typename PointT , typename Dist >

pcl::KdTreeFLANN< PointT, Dist >::KdTreeFLANN ( const KdTreeFLANN< PointT, Dist > & k )

Copy constructor.

Parameters

[in] k the tree to copy into this

Definition at line 70 of file kdtree_flann.hpp.

◆ ~KdTreeFLANN()

template<typename PointT , typename Dist = ::flann::L2_Simple<float>>

pcl::KdTreeFLANN< PointT, Dist >::~KdTreeFLANN ( )

inlineoverride

Destructor for KdTreeFLANN.

Deletes all allocated data arrays and destroys the kd-tree structures.

Definition at line 203 of file kdtree_flann.h.

Member Function Documentation

◆ makeShared()

template<typename PointT , typename Dist = ::flann::L2_Simple<float>>

Ptr pcl::KdTreeFLANN< PointT, Dist >::makeShared ( )

inline

Definition at line 195 of file kdtree_flann.h.

◆ nearestKSearch()

template<typename PointT , typename Dist >

int pcl::KdTreeFLANN< PointT, Dist >::nearestKSearch	(	const PointT &	point,
		unsigned int	k,
		Indices &	k_indices,
		std::vector< float > &	k_sqr_distances
	)		const

overridevirtual

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

Attention: This method does not do any bounds checking for the input index (i.e., index >= cloud.size () || index < 0), and assumes valid (i.e., finite) data.

Parameters

[in]	point	a given valid (i.e., finite) 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

Exceptions

asserts in debug mode if the index is not between 0 and the maximum number of points

Implements pcl::KdTree< PointT >.

Definition at line 235 of file kdtree_flann.hpp.

Referenced by pcl::gpu::DataSource::findKNNeghbors(), and pcl::getApproximateIndices().

◆ operator=()

template<typename PointT , typename Dist = ::flann::L2_Simple<float>>

KdTreeFLANN<PointT, Dist>& pcl::KdTreeFLANN< PointT, Dist >::operator= ( const KdTreeFLANN< PointT, Dist > & k )

inline

Copy operator.

Parameters

[in] k the tree to copy into this

Definition at line 171 of file kdtree_flann.h.

◆ radiusSearch()

template<typename PointT , typename Dist >

int pcl::KdTreeFLANN< PointT, Dist >::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.

Attention: This method does not do any bounds checking for the input index (i.e., index >= cloud.size () || index < 0), and assumes valid (i.e., finite) data.

Parameters

[in]	point	a given valid (i.e., finite) 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

Exceptions

asserts in debug mode if the index is not between 0 and the maximum number of points

Implements pcl::KdTree< PointT >.

Definition at line 373 of file kdtree_flann.hpp.

References pcl::radius_search().

Referenced by pcl::gpu::DataSource::findRadiusNeghbors(), and pcl::TextureMapping< PointInT >::textureMeshwithMultipleCameras().

◆ setEpsilon()

template<typename PointT , typename Dist >

void pcl::KdTreeFLANN< PointT, Dist >::setEpsilon ( float eps )

overridevirtual

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

Parameters

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

Reimplemented from pcl::KdTree< PointT >.

Definition at line 82 of file kdtree_flann.hpp.

◆ setInputCloud()

template<typename PointT , typename Dist >

void pcl::KdTreeFLANN< PointT, Dist >::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 - if NULL the whole cloud is used

Reimplemented from pcl::KdTree< PointT >.

Definition at line 100 of file kdtree_flann.hpp.

Referenced by pcl::gpu::DataSource::findKNNeghbors(), pcl::gpu::DataSource::findRadiusNeghbors(), pcl::getApproximateIndices(), and pcl::TextureMapping< PointInT >::textureMeshwithMultipleCameras().

◆ setSortedResults()

template<typename PointT , typename Dist >

void pcl::KdTreeFLANN< PointT, Dist >::setSortedResults ( bool sorted )

Definition at line 91 of file kdtree_flann.hpp.

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

pcl/kdtree/kdtree_flann.h
pcl/kdtree/impl/kdtree_flann.hpp

Public Types

Public Member Functions

Additional Inherited Members

Detailed Description

template<typename PointT, typename Dist = ::flann::L2_Simple<float>> class pcl::KdTreeFLANN< PointT, Dist >

Member Typedef Documentation

◆ ConstPtr

◆ FLANNIndex

◆ IndicesConstPtr

◆ IndicesPtr

◆ PointCloud

◆ PointCloudConstPtr

◆ Ptr

Constructor & Destructor Documentation

◆ KdTreeFLANN() [1/2]

◆ KdTreeFLANN() [2/2]

◆ ~KdTreeFLANN()

Member Function Documentation

◆ makeShared()

◆ nearestKSearch()

◆ operator=()

◆ radiusSearch()

◆ setEpsilon()

◆ setInputCloud()

◆ setSortedResults()

template<typename PointT, typename Dist = ::flann::L2_Simple<float>>
class pcl::KdTreeFLANN< PointT, Dist >