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
typedef boost::shared_ptr < KdTreeFLANN< PointT > >	Ptr
typedef boost::shared_ptr < const KdTreeFLANN< PointT > >	ConstPtr
typedef boost::shared_ptr < PointCloud >	PointCloudPtr
typedef pcl::PointRepresentation < PointT >	PointRepresentation
typedef boost::shared_ptr < const PointRepresentation >	PointRepresentationConstPtr
Public Member Functions
	KdTreeFLANN (bool sorted=true)
	Default Constructor for KdTreeFLANN.
	KdTreeFLANN (KdTreeFLANN &tree)
	Copy constructor.
void	setEpsilon (double eps)
	Set the search epsilon precision (error bound) for nearest neighbors searches.
Ptr	makeShared ()
KdTreeFLANN &	operator= (const KdTreeFLANN &tree)
	Asignment operator (shallow copy)
void	shallowCopy (const KdTreeFLANN &tree)
	Perform a shallow copy of the tree.
virtual	~KdTreeFLANN ()
	Destructor for KdTreeFLANN.
void	setInputCloud (const PointCloudConstPtr &cloud, const IndicesConstPtr &indices=IndicesConstPtr())
	Provide a pointer to the input dataset.
int	nearestKSearch (const PointT &point, int k, std::vector< int > &k_indices, std::vector< float > &k_sqr_distances)
	Search for k-nearest neighbors for the given query point.
int	nearestKSearch (const PointCloud &cloud, int index, int k, std::vector< int > &k_indices, std::vector< float > &k_sqr_distances)
	Search for k-nearest neighbors for the given query point.
int	nearestKSearch (int index, int k, std::vector< int > &k_indices, std::vector< float > &k_sqr_distances)
	Search for k-nearest neighbors for the given query point (zero-copy).
int	radiusSearch (const PointT &point, double radius, std::vector< int > &k_indices, std::vector< float > &k_sqr_distances, int max_nn=-1) const
	Search for all the nearest neighbors of the query point in a given radius.
int	radiusSearch (const PointCloud &cloud, int index, double radius, std::vector< int > &k_indices, std::vector< float > &k_sqr_distances, int max_nn=-1) const
	Search for all the nearest neighbors of the query point in a given radius.
int	radiusSearch (int index, double radius, std::vector< int > &k_indices, std::vector< float > &k_sqr_distances, int max_nn=-1) const
	Search for all the nearest neighbors of the query point in a given radius (zero-copy).
IndicesConstPtr const	getIndices ()
	Get a pointer to the vector of indices used.
PointCloudConstPtr	getInputCloud ()
	Get a pointer to the input point cloud dataset.
void	setPointRepresentation (const PointRepresentationConstPtr &point_representation)
	Provide a pointer to the point representation to use to convert points into k-D vectors.
PointRepresentationConstPtr const	getPointRepresentation ()
	Get a pointer to the point representation used when converting points into k-D vectors.
template<typename PointTDiff >
int	nearestKSearchT (const PointTDiff &point, int k, std::vector< int > &k_indices, std::vector< float > &k_sqr_distances)
	Search for k-nearest neighbors for the given query point.
template<typename PointTDiff >
int	radiusSearchT (const PointTDiff &point, double radius, std::vector< int > &k_indices, std::vector< float > &k_sqr_distances, int max_nn=-1) const
	Search for all the nearest neighbors of the query point in a given radius.
double	getEpsilon ()
	Get the search epsilon precision (error bound) for nearest neighbors searches.
void	setMinPts (int min_pts)
	Minimum allowed number of k nearest neighbors points that a viable result must contain.
float	getMinPts ()
	Get the minimum allowed number of k nearest neighbors points that a viable result must contain.

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 57 of file kdtree_flann.h.

Member Typedef Documentation

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

typedef boost::shared_ptr<const KdTreeFLANN<PointT> > pcl::KdTreeFLANN< PointT, Dist >::ConstPtr

Reimplemented from pcl::KdTree< PointT >.

Definition at line 76 of file kdtree_flann.h.

template<typename PointT>

typedef boost::shared_ptr<PointCloud> pcl::KdTree< PointT >::PointCloudPtr [inherited]

Definition at line 63 of file kdtree.h.

template<typename PointT>

typedef pcl::PointRepresentation<PointT> pcl::KdTree< PointT >::PointRepresentation [inherited]

Definition at line 66 of file kdtree.h.

template<typename PointT>

typedef boost::shared_ptr<const PointRepresentation> pcl::KdTree< PointT >::PointRepresentationConstPtr [inherited]

Definition at line 68 of file kdtree.h.

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

typedef boost::shared_ptr<KdTreeFLANN<PointT> > pcl::KdTreeFLANN< PointT, Dist >::Ptr

Reimplemented from pcl::KdTree< PointT >.

Definition at line 75 of file kdtree_flann.h.

Constructor & Destructor Documentation

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

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

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 83 of file kdtree_flann.h.

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

pcl::KdTreeFLANN< PointT, Dist >::KdTreeFLANN ( KdTreeFLANN< PointT, Dist > & tree ) [inline]

Copy constructor.

This copy constructor does shallow copy of the tree, the only reason why it's needed is because boost::mutex is non-copyable, so the default copy constructor would not work

Parameters:

[in] tree the tree to copy

Definition at line 98 of file kdtree_flann.h.

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

virtual pcl::KdTreeFLANN< PointT, Dist >::~KdTreeFLANN ( ) [inline, virtual]

Destructor for KdTreeFLANN.

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

Definition at line 144 of file kdtree_flann.h.

Member Function Documentation

template<typename PointT>

double pcl::KdTree< PointT >::getEpsilon ( ) [inline, inherited]

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

Definition at line 258 of file kdtree.h.

template<typename PointT>

IndicesConstPtr const pcl::KdTree< PointT >::getIndices ( ) [inline, inherited]

Get a pointer to the vector of indices used.

Definition at line 97 of file kdtree.h.

template<typename PointT>

PointCloudConstPtr pcl::KdTree< PointT >::getInputCloud ( ) [inline, inherited]

Get a pointer to the input point cloud dataset.

Definition at line 104 of file kdtree.h.

template<typename PointT>

float pcl::KdTree< PointT >::getMinPts ( ) [inline, inherited]

Get the minimum allowed number of k nearest neighbors points that a viable result must contain.

Definition at line 274 of file kdtree.h.

template<typename PointT>

PointRepresentationConstPtr const pcl::KdTree< PointT >::getPointRepresentation ( ) [inline, inherited]

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

Definition at line 121 of file kdtree.h.

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

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

Definition at line 114 of file kdtree_flann.h.

template<typename PointT, typename Dist >

int pcl::KdTreeFLANN< PointT, Dist >::nearestKSearch	(	const PointT &	point,
		int	k,
		std::vector< int > &	k_indices,
		std::vector< float > &	k_sqr_distances
	)		`[virtual]`

Search for 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::KdTree< PointT >.

Definition at line 77 of file kdtree_flann.hpp.

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

int pcl::KdTreeFLANN< PointT, Dist >::nearestKSearch	(	const PointCloud &	cloud,
		int	index,
		int	k,
		std::vector< int > &	k_indices,
		std::vector< float > &	k_sqr_distances
	)		`[inline, virtual]`

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

Parameters:

[in]	cloud	the point cloud data
[in]	index	the index in cloud representing the 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::KdTree< PointT >.

Definition at line 178 of file kdtree_flann.h.

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

int pcl::KdTreeFLANN< PointT, Dist >::nearestKSearch	(	int	index,
		int	k,
		std::vector< int > &	k_indices,
		std::vector< float > &	k_sqr_distances
	)		`[inline, virtual]`

Search for k-nearest neighbors for the given query point (zero-copy).

Parameters:

[in]	index	the index representing the query point in the dataset given by setInputCloud if indices were given in setInputCloud, index will be the position in the indices vector
[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::KdTree< PointT >.

Definition at line 196 of file kdtree_flann.h.

template<typename PointT>

template<typename PointTDiff >

int pcl::KdTree< PointT >::nearestKSearchT	(	const PointTDiff &	point,
		int	k,
		std::vector< int > &	k_indices,
		std::vector< float > &	k_sqr_distances
	)		`[inline, inherited]`

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

This method accepts a different template parameter for the point type.

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

Definition at line 164 of file kdtree.h.

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

KdTreeFLANN& pcl::KdTreeFLANN< PointT, Dist >::operator= ( const KdTreeFLANN< PointT, Dist > & tree ) [inline]

Asignment operator (shallow copy)

Parameters:

[in] tree the tree to copy

Definition at line 119 of file kdtree_flann.h.

template<typename PointT, typename Dist >

int pcl::KdTreeFLANN< PointT, Dist >::radiusSearch	(	const PointT &	point,
		double	radius,
		std::vector< int > &	k_indices,
		std::vector< float > &	k_sqr_distances,
		int	max_nn = `-1`
	)		const `[virtual]`

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

Returns:: number of neighbors found in radius

Implements pcl::KdTree< PointT >.

Definition at line 117 of file kdtree_flann.hpp.

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

int pcl::KdTreeFLANN< PointT, Dist >::radiusSearch	(	const PointCloud &	cloud,
		int	index,
		double	radius,
		std::vector< int > &	k_indices,
		std::vector< float > &	k_sqr_distances,
		int	max_nn = `-1`
	)		const `[inline, virtual]`

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

Parameters:

[in]	cloud	the point cloud data
[in]	index	the index in cloud representing the 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

Returns:: number of neighbors found in radius

Implements pcl::KdTree< PointT >.

Definition at line 235 of file kdtree_flann.h.

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

int pcl::KdTreeFLANN< PointT, Dist >::radiusSearch	(	int	index,
		double	radius,
		std::vector< int > &	k_indices,
		std::vector< float > &	k_sqr_distances,
		int	max_nn = `-1`
	)		const `[inline, virtual]`

Search for all the nearest neighbors of the query point in a given radius (zero-copy).

Parameters:

[in]	index	the index representing the query point in the dataset given by setInputCloud if indices were given in setInputCloud, index will be the position in the indices vector
[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

Returns:: number of neighbors found in radius

Implements pcl::KdTree< PointT >.

Definition at line 254 of file kdtree_flann.h.

template<typename PointT>

template<typename PointTDiff >

int pcl::KdTree< PointT >::radiusSearchT	(	const PointTDiff &	point,
		double	radius,
		std::vector< int > &	k_indices,
		std::vector< float > &	k_sqr_distances,
		int	max_nn = `-1`
	)		const `[inline, inherited]`

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

Returns:: number of neighbors found in radius

Definition at line 222 of file kdtree.h.

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

void pcl::KdTreeFLANN< PointT, Dist >::setEpsilon ( double eps ) [inline, virtual]

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 107 of file kdtree_flann.h.

template<typename PointT , typename Dist >

void pcl::KdTreeFLANN< PointT, Dist >::setInputCloud	(	const PointCloudConstPtr &	cloud,
		const IndicesConstPtr &	indices = `IndicesConstPtr ()`
	)		`[virtual]`

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 46 of file kdtree_flann.hpp.

template<typename PointT>

void pcl::KdTree< PointT >::setMinPts ( int min_pts ) [inline, inherited]

Minimum allowed number of k nearest neighbors points that a viable result must contain.

Parameters:

[in] min_pts the minimum number of neighbors in a viable neighborhood

Definition at line 267 of file kdtree.h.

template<typename PointT>

void pcl::KdTree< PointT >::setPointRepresentation ( const PointRepresentationConstPtr & point_representation ) [inline, inherited]

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 113 of file kdtree.h.

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

void pcl::KdTreeFLANN< PointT, Dist >::shallowCopy ( const KdTreeFLANN< PointT, Dist > & tree ) [inline]

Perform a shallow copy of the tree.

Parameters:

[in] tree the tree to copy

Definition at line 131 of file kdtree_flann.h.

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

/builddir/build/BUILD/PCL-1.4.0-Source/kdtree/include/pcl/kdtree/kdtree_flann.h
/builddir/build/BUILD/PCL-1.4.0-Source/kdtree/include/pcl/kdtree/impl/kdtree_flann.hpp

Public Types

Public Member Functions

Detailed Description

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

Member Typedef Documentation

Constructor & Destructor Documentation

Member Function Documentation

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