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 KdTree< PointT > ::PointCloud	PointCloud
typedef KdTree< PointT > ::PointCloudConstPtr	PointCloudConstPtr
typedef boost::shared_ptr < std::vector< int > >	IndicesPtr
typedef boost::shared_ptr < const std::vector< int > >	IndicesConstPtr
typedef flann::Index< Dist >	FLANNIndex
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 (const KdTreeFLANN< PointT > &k)
	Copy constructor.
KdTreeFLANN< PointT > &	operator= (const KdTreeFLANN< PointT > &k)
	Copy operator.
void	setEpsilon (float eps)
	Set the search epsilon precision (error bound) for nearest neighbors searches.
void	setSortedResults (bool sorted)
Ptr	makeShared ()
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) const
	Search for k-nearest neighbors for the given query point.
int	radiusSearch (const PointT &point, double radius, std::vector< int > &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.
IndicesConstPtr	getIndices () const
	Get a pointer to the vector of indices used.
PointCloudConstPtr	getInputCloud () const
	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	getPointRepresentation () const
	Get a pointer to the point representation used when converting points into k-D vectors.
virtual int	nearestKSearch (const PointCloud &cloud, int index, int k, std::vector< int > &k_indices, std::vector< float > &k_sqr_distances) const
	Search for k-nearest neighbors for the given query point.
virtual int	nearestKSearch (int index, int k, std::vector< int > &k_indices, std::vector< float > &k_sqr_distances) const
	Search for k-nearest neighbors for the given query point (zero-copy).
template<typename PointTDiff >
int	nearestKSearchT (const PointTDiff &point, int k, std::vector< int > &k_indices, std::vector< float > &k_sqr_distances) const
	Search for k-nearest neighbors for the given query point.
virtual int	radiusSearch (const PointCloud &cloud, int index, double radius, std::vector< int > &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 (int index, double radius, std::vector< int > &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).
template<typename PointTDiff >
int	radiusSearchT (const PointTDiff &point, double radius, std::vector< int > &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.
float	getEpsilon () const
	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.
int	getMinPts () const
	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 78 of file kdtree_flann.h.

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

typedef flann::Index<Dist> pcl::KdTreeFLANN< PointT, Dist >::FLANNIndex

Definition at line 74 of file kdtree_flann.h.

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

typedef boost::shared_ptr<const std::vector<int> > pcl::KdTreeFLANN< PointT, Dist >::IndicesConstPtr

Reimplemented from pcl::KdTree< PointT >.

Definition at line 72 of file kdtree_flann.h.

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

typedef boost::shared_ptr<std::vector<int> > pcl::KdTreeFLANN< PointT, Dist >::IndicesPtr

Reimplemented from pcl::KdTree< PointT >.

Definition at line 71 of file kdtree_flann.h.

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

typedef KdTree<PointT>::PointCloud pcl::KdTreeFLANN< PointT, Dist >::PointCloud

Reimplemented from pcl::KdTree< PointT >.

Definition at line 68 of file kdtree_flann.h.

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

typedef KdTree<PointT>::PointCloudConstPtr pcl::KdTreeFLANN< PointT, Dist >::PointCloudConstPtr

Reimplemented from pcl::KdTree< PointT >.

Definition at line 69 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 77 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 85 of file kdtree_flann.h.

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

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

Copy constructor.

Parameters:

[in] tree the tree to copy into this

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 151 of file kdtree_flann.h.

Member Function Documentation

template<typename PointT>

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

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

Definition at line 328 of file kdtree.h.

template<typename PointT>

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

Get a pointer to the vector of indices used.

Definition at line 96 of file kdtree.h.

template<typename PointT>

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

Get a pointer to the input point cloud dataset.

Definition at line 103 of file kdtree.h.

template<typename PointT>

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

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

Definition at line 344 of file kdtree.h.

template<typename PointT>

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

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

Definition at line 120 of file kdtree.h.

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

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

Definition at line 146 of file kdtree_flann.h.

template<typename PointT>

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

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.points.size () || index < 0), and assumes valid (i.e., finite) data.

Parameters:

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

Definition at line 157 of file kdtree.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
	)		const `[virtual]`

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.points.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 79 of file kdtree_flann.hpp.

template<typename PointT>

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

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

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

Parameters:

[in]	index	a valid index representing a valid 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

Exceptions:

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

Definition at line 204 of file kdtree.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
	)		const `[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 174 of file kdtree.h.

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

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

Copy operator.

Parameters:

[in] tree the tree to copy into this

Definition at line 113 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,
		unsigned int	max_nn = `0`
	)		const `[virtual]`

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.points.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 116 of file kdtree_flann.hpp.

template<typename PointT>

virtual int pcl::KdTree< PointT >::radiusSearch	(	const PointCloud &	cloud,
		int	index,
		double	radius,
		std::vector< int > &	k_indices,
		std::vector< float > &	k_sqr_distances,
		unsigned int	max_nn = `0`
	)		const `[inline, virtual, inherited]`

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.points.size () || index < 0), and assumes valid (i.e., finite) data.

Parameters:

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

Definition at line 251 of file kdtree.h.

template<typename PointT>

virtual int pcl::KdTree< PointT >::radiusSearch	(	int	index,
		double	radius,
		std::vector< int > &	k_indices,
		std::vector< float > &	k_sqr_distances,
		unsigned int	max_nn = `0`
	)		const `[inline, virtual, inherited]`

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

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

Parameters:

[in]	index	a valid index representing a valid 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. 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

Definition at line 302 of file kdtree.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,
		unsigned int	max_nn = `0`
	)		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. 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

Definition at line 270 of file kdtree.h.

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

void pcl::KdTreeFLANN< PointT, Dist >::setEpsilon ( float 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 131 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 337 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 112 of file kdtree.h.

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

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

Definition at line 139 of file kdtree_flann.h.

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

/usr/src/RPM/BUILD/PCL-1.6.0-Source/kdtree/include/pcl/kdtree/kdtree_flann.h
/usr/src/RPM/BUILD/PCL-1.6.0-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 >