KdTree represents the base spatial locator class for kd-tree implementations. More...

#include <kdtree.h>

Inheritance diagram for pcl::KdTree< PointT >:

Public Types
typedef boost::shared_ptr < const KdTree< PointT > >	ConstPtr
typedef boost::shared_ptr < const std::vector< int > >	IndicesConstPtr
typedef boost::shared_ptr < std::vector< int > >	IndicesPtr
typedef pcl::PointCloud< PointT >	PointCloud
typedef boost::shared_ptr < const PointCloud >	PointCloudConstPtr
typedef boost::shared_ptr < PointCloud >	PointCloudPtr
typedef pcl::PointRepresentation < PointT >	PointRepresentation
typedef boost::shared_ptr < const PointRepresentation >	PointRepresentationConstPtr
typedef boost::shared_ptr < KdTree< PointT > >	Ptr
Public Member Functions
float	getEpsilon () const
	Get the search epsilon precision (error bound) for nearest neighbors searches.
IndicesConstPtr	getIndices () const
	Get a pointer to the vector of indices used.
PointCloudConstPtr	getInputCloud () const
	Get a pointer to the input point cloud dataset.
int	getMinPts () const
	Get the minimum allowed number of k nearest neighbors points that a viable result must contain.
PointRepresentationConstPtr	getPointRepresentation () const
	Get a pointer to the point representation used when converting points into k-D vectors.
	KdTree (bool sorted=true)
	Empty constructor for KdTree. Sets some internal values to their defaults.
virtual int	nearestKSearch (const PointT &p_q, int k, std::vector< int > &k_indices, std::vector< float > &k_sqr_distances) const =0
	Search for k-nearest neighbors for the given query point.
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. This method accepts a different template parameter for the point type.
virtual int	radiusSearch (const PointT &p_q, double radius, std::vector< int > &k_indices, std::vector< float > &k_sqr_distances, unsigned int max_nn=0) const =0
	Search for all the nearest neighbors of the query point in a given radius.
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.
virtual void	setEpsilon (float eps)
	Set the search epsilon precision (error bound) for nearest neighbors searches.
virtual void	setInputCloud (const PointCloudConstPtr &cloud, const IndicesConstPtr &indices=IndicesConstPtr())
	Provide a pointer to the input dataset.
void	setMinPts (int min_pts)
	Minimum allowed number of k nearest neighbors points that a viable result must contain.
void	setPointRepresentation (const PointRepresentationConstPtr &point_representation)
	Provide a pointer to the point representation to use to convert points into k-D vectors.
virtual	~KdTree ()
	Destructor for KdTree. Deletes all allocated data arrays and destroys the kd-tree structures.
Protected Member Functions
virtual std::string	getName () const =0
	Class getName method.
Protected Attributes
float	epsilon_
	Epsilon precision (error bound) for nearest neighbors searches.
IndicesConstPtr	indices_
	A pointer to the vector of point indices to use.
PointCloudConstPtr	input_
	The input point cloud dataset containing the points we need to use.
int	min_pts_
	Minimum allowed number of k nearest neighbors points that a viable result must contain.
PointRepresentationConstPtr	point_representation_
	For converting different point structures into k-dimensional vectors for nearest-neighbor search.
bool	sorted_
	Return the radius search neighbours sorted.

Detailed Description

template<typename PointT>
class pcl::KdTree< PointT >

KdTree represents the base spatial locator class for kd-tree implementations.

Author:: Radu B Rusu, Bastian Steder, Michael Dixon

Definition at line 56 of file kdtree/include/pcl/kdtree/kdtree.h.

Member Typedef Documentation

template<typename PointT>

typedef boost::shared_ptr<const KdTree<PointT> > pcl::KdTree< PointT >::ConstPtr

Reimplemented in pcl::KdTreeFLANN< PointT, Dist >, pcl::KdTreeFLANN< PointTarget >, pcl::KdTreeFLANN< FeatureT >, pcl::KdTreeFLANN< GlobalDescriptorT >, and pcl::KdTreeFLANN< pcl::FPFHSignature33 >.

Definition at line 72 of file kdtree/include/pcl/kdtree/kdtree.h.

template<typename PointT>

typedef boost::shared_ptr<const std::vector<int> > pcl::KdTree< PointT >::IndicesConstPtr

Reimplemented in pcl::KdTreeFLANN< PointT, Dist >, pcl::KdTreeFLANN< PointTarget >, pcl::KdTreeFLANN< FeatureT >, pcl::KdTreeFLANN< GlobalDescriptorT >, and pcl::KdTreeFLANN< pcl::FPFHSignature33 >.

Definition at line 60 of file kdtree/include/pcl/kdtree/kdtree.h.

template<typename PointT>

typedef boost::shared_ptr<std::vector<int> > pcl::KdTree< PointT >::IndicesPtr

Reimplemented in pcl::KdTreeFLANN< PointT, Dist >, pcl::KdTreeFLANN< PointTarget >, pcl::KdTreeFLANN< FeatureT >, pcl::KdTreeFLANN< GlobalDescriptorT >, and pcl::KdTreeFLANN< pcl::FPFHSignature33 >.

Definition at line 59 of file kdtree/include/pcl/kdtree/kdtree.h.

template<typename PointT>

typedef pcl::PointCloud<PointT> pcl::KdTree< PointT >::PointCloud

Reimplemented in pcl::KdTreeFLANN< PointT, Dist >, pcl::KdTreeFLANN< PointTarget >, pcl::KdTreeFLANN< FeatureT >, pcl::KdTreeFLANN< GlobalDescriptorT >, and pcl::KdTreeFLANN< pcl::FPFHSignature33 >.

Definition at line 62 of file kdtree/include/pcl/kdtree/kdtree.h.

template<typename PointT>

typedef boost::shared_ptr<const PointCloud> pcl::KdTree< PointT >::PointCloudConstPtr

Reimplemented in pcl::KdTreeFLANN< PointT, Dist >, pcl::KdTreeFLANN< PointTarget >, pcl::KdTreeFLANN< FeatureT >, pcl::KdTreeFLANN< GlobalDescriptorT >, and pcl::KdTreeFLANN< pcl::FPFHSignature33 >.

Definition at line 64 of file kdtree/include/pcl/kdtree/kdtree.h.

template<typename PointT>

typedef boost::shared_ptr<PointCloud> pcl::KdTree< PointT >::PointCloudPtr

Definition at line 63 of file kdtree/include/pcl/kdtree/kdtree.h.

template<typename PointT>

typedef pcl::PointRepresentation<PointT> pcl::KdTree< PointT >::PointRepresentation

Definition at line 66 of file kdtree/include/pcl/kdtree/kdtree.h.

template<typename PointT>

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

Definition at line 68 of file kdtree/include/pcl/kdtree/kdtree.h.

template<typename PointT>

typedef boost::shared_ptr<KdTree<PointT> > pcl::KdTree< PointT >::Ptr

Reimplemented in pcl::KdTreeFLANN< PointT, Dist >, pcl::KdTreeFLANN< PointTarget >, pcl::KdTreeFLANN< FeatureT >, pcl::KdTreeFLANN< GlobalDescriptorT >, and pcl::KdTreeFLANN< pcl::FPFHSignature33 >.

Definition at line 71 of file kdtree/include/pcl/kdtree/kdtree.h.

Constructor & Destructor Documentation

template<typename PointT>

pcl::KdTree< PointT >::KdTree ( bool sorted = true ) [inline]

Empty constructor for KdTree. Sets some internal values to their defaults.

Parameters:

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

Definition at line 77 of file kdtree/include/pcl/kdtree/kdtree.h.

template<typename PointT>

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

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

Definition at line 126 of file kdtree/include/pcl/kdtree/kdtree.h.

Member Function Documentation

template<typename PointT>

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

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

Definition at line 328 of file kdtree/include/pcl/kdtree/kdtree.h.

template<typename PointT>

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

Get a pointer to the vector of indices used.

Definition at line 96 of file kdtree/include/pcl/kdtree/kdtree.h.

template<typename PointT>

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

Get a pointer to the input point cloud dataset.

Definition at line 103 of file kdtree/include/pcl/kdtree/kdtree.h.

template<typename PointT>

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

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

Definition at line 344 of file kdtree/include/pcl/kdtree/kdtree.h.

template<typename PointT>

virtual std::string pcl::KdTree< PointT >::getName ( ) const [protected, pure virtual]

Class getName method.

Implemented in pcl::KdTreeFLANN< PointT, Dist >, pcl::KdTreeFLANN< PointTarget >, pcl::KdTreeFLANN< FeatureT >, pcl::KdTreeFLANN< GlobalDescriptorT >, and pcl::KdTreeFLANN< pcl::FPFHSignature33 >.

template<typename PointT>

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

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

Definition at line 120 of file kdtree/include/pcl/kdtree/kdtree.h.

template<typename PointT>

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

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

Parameters:

[in]	p_q	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

Implemented in pcl::KdTreeFLANN< PointT, Dist >, pcl::KdTreeFLANN< PointTarget >, pcl::KdTreeFLANN< FeatureT >, pcl::KdTreeFLANN< GlobalDescriptorT >, and pcl::KdTreeFLANN< pcl::FPFHSignature33 >.

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]`

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/include/pcl/kdtree/kdtree.h.

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]`

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/include/pcl/kdtree/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]`

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/include/pcl/kdtree/kdtree.h.

template<typename PointT>

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

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

Parameters:

[in]	p_q	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

Implemented in pcl::KdTreeFLANN< PointT, Dist >, pcl::KdTreeFLANN< PointTarget >, pcl::KdTreeFLANN< FeatureT >, pcl::KdTreeFLANN< GlobalDescriptorT >, and pcl::KdTreeFLANN< pcl::FPFHSignature33 >.

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]`

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/include/pcl/kdtree/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]`

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/include/pcl/kdtree/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]`

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/include/pcl/kdtree/kdtree.h.

template<typename PointT>

virtual void pcl::KdTree< PointT >::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 in pcl::KdTreeFLANN< PointT, Dist >, pcl::KdTreeFLANN< PointTarget >, pcl::KdTreeFLANN< FeatureT >, pcl::KdTreeFLANN< GlobalDescriptorT >, and pcl::KdTreeFLANN< pcl::FPFHSignature33 >.

Definition at line 321 of file kdtree/include/pcl/kdtree/kdtree.h.

template<typename PointT>

virtual void pcl::KdTree< PointT >::setInputCloud	(	const PointCloudConstPtr &	cloud,
		const IndicesConstPtr &	indices = `IndicesConstPtr ()`
	)		`[inline, 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 in pcl::KdTreeFLANN< PointT, Dist >, pcl::KdTreeFLANN< PointTarget >, pcl::KdTreeFLANN< FeatureT >, pcl::KdTreeFLANN< GlobalDescriptorT >, and pcl::KdTreeFLANN< pcl::FPFHSignature33 >.

Definition at line 88 of file kdtree/include/pcl/kdtree/kdtree.h.

template<typename PointT>

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