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 >:

[legend]

Collaboration diagram for pcl::search::KdTree< PointT >:

[legend]

List of all members.

Public Types
typedef Search< PointT > ::PointCloud	PointCloud
typedef Search< PointT > ::PointCloudConstPtr	PointCloudConstPtr
typedef boost::shared_ptr < std::vector< int > >	IndicesPtr
typedef boost::shared_ptr < const std::vector< int > >	IndicesConstPtr
typedef boost::shared_ptr < KdTree< PointT > >	Ptr
typedef boost::shared_ptr < const KdTree< PointT > >	ConstPtr
typedef boost::shared_ptr < pcl::KdTreeFLANN< PointT > >	KdTreeFLANNPtr
typedef boost::shared_ptr < const pcl::KdTreeFLANN < PointT > >	KdTreeFLANNConstPtr
typedef PointCloud::Ptr	PointCloudPtr
Public Member Functions
	KdTree (bool sorted=true)
	Constructor for KdTree.
virtual	~KdTree ()
	Destructor for KdTree.
virtual void	setSortedResults (bool sorted_results)
	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.
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 the 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.
virtual const std::string &	getName () const
	returns the search method name
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.
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).
virtual void	nearestKSearch (const PointCloud &cloud, const std::vector< int > &indices, int k, std::vector< std::vector< int > > &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	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.
template<typename PointTDiff >
void	nearestKSearchT (const pcl::PointCloud< PointTDiff > &cloud, const std::vector< int > &indices, int k, std::vector< std::vector< int > > &k_indices, std::vector< std::vector< float > > &k_sqr_distances) const
	Search for the 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).
virtual void	radiusSearch (const PointCloud &cloud, const std::vector< int > &indices, double radius, std::vector< std::vector< int > > &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 >
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.
template<typename PointTDiff >
void	radiusSearchT (const pcl::PointCloud< PointTDiff > &cloud, const std::vector< int > &indices, double radius, std::vector< std::vector< int > > &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.

Detailed Description

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

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 59 of file kdtree.h.

Member Typedef Documentation

template<typename PointT>

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

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

Definition at line 77 of file kdtree.h.

template<typename PointT>

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

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

Definition at line 66 of file kdtree.h.

template<typename PointT>

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

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

Definition at line 65 of file kdtree.h.

template<typename PointT>

typedef boost::shared_ptr<const pcl::KdTreeFLANN<PointT> > pcl::search::KdTree< PointT >::KdTreeFLANNConstPtr

Definition at line 80 of file kdtree.h.

template<typename PointT>

typedef boost::shared_ptr<pcl::KdTreeFLANN<PointT> > pcl::search::KdTree< PointT >::KdTreeFLANNPtr

Definition at line 79 of file kdtree.h.

template<typename PointT>

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

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

Definition at line 62 of file kdtree.h.

template<typename PointT>

typedef Search<PointT>::PointCloudConstPtr pcl::search::KdTree< PointT >::PointCloudConstPtr

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

Definition at line 63 of file kdtree.h.

template<typename PointT>

typedef PointCloud::Ptr pcl::search::Search< PointT >::PointCloudPtr [inherited]

Reimplemented in pcl::search::Octree< PointT, LeafTWrap, BranchTWrap, OctreeT >, and pcl::search::OrganizedNeighbor< PointT >.

Definition at line 77 of file search.h.

template<typename PointT>

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

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

Definition at line 76 of file kdtree.h.

Constructor & Destructor Documentation

template<typename PointT>

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

Constructor for KdTree.

Parameters:

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 89 of file kdtree.h.

template<typename PointT>

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

Destructor for KdTree.

Definition at line 97 of file kdtree.h.

Member Function Documentation

template<typename PointT>

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

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

Definition at line 122 of file kdtree.h.

template<typename PointT>

virtual IndicesConstPtr pcl::search::Search< PointT >::getIndices ( ) const [inline, virtual, inherited]

Get a pointer to the vector of indices used.

Definition at line 139 of file search.h.

template<typename PointT>

virtual PointCloudConstPtr pcl::search::Search< PointT >::getInputCloud ( ) const [inline, virtual, inherited]

Get a pointer to the input point cloud dataset.

Definition at line 132 of file search.h.

template<typename PointT>

virtual const std::string& pcl::search::Search< PointT >::getName ( ) const [inline, virtual, inherited]

returns the search method name

Definition at line 104 of file search.h.

template<typename PointT>

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

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 >.

Definition at line 148 of file kdtree.h.

template<typename PointT>

virtual int pcl::search::Search< 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

Reimplemented in pcl::search::Octree< PointT, LeafTWrap, BranchTWrap, OctreeT >.

Definition at line 195 of file search.h.

template<typename PointT>

virtual int pcl::search::Search< 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

Reimplemented in pcl::search::Octree< PointT, LeafTWrap, BranchTWrap, OctreeT >.

Definition at line 220 of file search.h.

template<typename PointT>

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

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

Parameters:

[in]	cloud	the point cloud data
[in]	indices	a vector of point cloud indices to query for nearest neighbors
[in]	k	the number of neighbors to search for
[out]	k_indices	the resultant indices of the neighboring points, k_indices[i] corresponds to the neighbors of the query point i
[out]	k_sqr_distances	the resultant squared distances to the neighboring points, k_sqr_distances[i] corresponds to the neighbors of the query point i

Definition at line 245 of file search.h.

template<typename PointT>

template<typename PointTDiff >

int pcl::search::Search< 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 166 of file search.h.

template<typename PointT>

template<typename PointTDiff >

void pcl::search::Search< PointT >::nearestKSearchT	(	const pcl::PointCloud< PointTDiff > &	cloud,
		const std::vector< int > &	indices,
		int	k,
		std::vector< std::vector< int > > &	k_indices,
		std::vector< std::vector< float > > &	k_sqr_distances
	)		const `[inline, inherited]`

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

Use this method if the query points are of a different type than the points in the data set (e.g. PointXYZRGBA instead of PointXYZ).

Parameters:

[in]	cloud	the point cloud data
[in]	indices	a vector of point cloud indices to query for nearest neighbors
[in]	k	the number of neighbors to search for
[out]	k_indices	the resultant indices of the neighboring points, k_indices[i] corresponds to the neighbors of the query point i
[out]	k_sqr_distances	the resultant squared distances to the neighboring points, k_sqr_distances[i] corresponds to the neighbors of the query point i

Note:: This method copies the input point cloud of type PointTDiff to a temporary cloud of type PointT and performs the batch search on the new cloud. You should prefer the single-point search if you don't use a search algorithm that accelerates batch NN search.

Definition at line 273 of file search.h.

template<typename PointT>

int pcl::search::KdTree< PointT >::radiusSearch	(	const PointT &	point,
		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.

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 >.

Definition at line 164 of file kdtree.h.

template<typename PointT>

virtual int pcl::search::Search< 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

Reimplemented in pcl::search::Octree< PointT, LeafTWrap, BranchTWrap, OctreeT >.

Definition at line 359 of file search.h.

template<typename PointT>

virtual int pcl::search::Search< 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

Reimplemented in pcl::search::Octree< PointT, LeafTWrap, BranchTWrap, OctreeT >.

Definition at line 387 of file search.h.

template<typename PointT>

virtual void pcl::search::Search< PointT >::radiusSearch	(	const PointCloud &	cloud,
		const std::vector< int > &	indices,
		double	radius,
		std::vector< std::vector< int > > &	k_indices,
		std::vector< 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.

Parameters:

[in]	cloud	the point cloud data
[in]	indices	the indices in cloud. If indices is empty, neighbors will be searched for all points.
[in]	radius	the radius of the sphere bounding all of p_q's neighbors
[out]	k_indices	the resultant indices of the neighboring points, k_indices[i] corresponds to the neighbors of the query point i
[out]	k_sqr_distances	the resultant squared distances to the neighboring points, k_sqr_distances[i] corresponds to the neighbors of the query point i
[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.

Definition at line 413 of file search.h.

template<typename PointT>

template<typename PointTDiff >

int pcl::search::Search< 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 329 of file search.h.

template<typename PointT>

template<typename PointTDiff >

void pcl::search::Search< PointT >::radiusSearchT	(	const pcl::PointCloud< PointTDiff > &	cloud,
		const std::vector< int > &	indices,
		double	radius,
		std::vector< std::vector< int > > &	k_indices,
		std::vector< std::vector< float > > &	k_sqr_distances,
		unsigned int	max_nn = `0`
	)		const `[inline, inherited]`

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

Parameters:

[in]	cloud	the point cloud data
[in]	indices	a vector of point cloud indices to query for nearest neighbors
[in]	radius	the radius of the sphere bounding all of p_q's neighbors
[out]	k_indices	the resultant indices of the neighboring points, k_indices[i] corresponds to the neighbors of the query point i
[out]	k_sqr_distances	the resultant squared distances to the neighboring points, k_sqr_distances[i] corresponds to the neighbors of the query point i
[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.

Note:: This method copies the input point cloud of type PointTDiff to a temporary cloud of type PointT and performs the batch search on the new cloud. You should prefer the single-point search if you don't use a search algorithm that accelerates batch NN search.

Definition at line 449 of file search.h.

template<typename PointT>

void pcl::search::KdTree< PointT >::setEpsilon ( float eps ) [inline]

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

Parameters:

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

Definition at line 115 of file kdtree.h.

template<typename PointT>

void pcl::search::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

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

Definition at line 132 of file kdtree.h.

template<typename PointT>

virtual void pcl::search::KdTree< PointT >::setSortedResults ( bool sorted_results ) [inline, virtual]

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 >.

Definition at line 105 of file kdtree.h.

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

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

Public Types

Public Member Functions

Detailed Description

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

Member Typedef Documentation

Constructor & Destructor Documentation

Member Function Documentation

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