Point Cloud Library (PCL)  1.9.1-dev
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pcl::search::KdTree< PointT, Tree > Class Template Reference

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, Tree >:

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, Tree > > Ptr
 
typedef boost::shared_ptr< const KdTree< PointT, Tree > > ConstPtr
 
typedef boost::shared_ptr< Tree > KdTreePtr
 
typedef boost::shared_ptr< const Tree > KdTreeConstPtr
 
typedef boost::shared_ptr< const PointRepresentation< PointT > > PointRepresentationConstPtr
 

Public Member Functions

 KdTree (bool sorted=true)
 Constructor for KdTree. More...
 
virtual ~KdTree ()
 Destructor for KdTree. 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...
 
void setSortedResults (bool sorted_results)
 Sets whether the results have to be sorted or not. More...
 
void setEpsilon (float eps)
 Set the search epsilon precision (error bound) for nearest neighbors searches. More...
 
float getEpsilon () const
 Get the search epsilon precision (error bound) for nearest neighbors searches. More...
 
void setInputCloud (const PointCloudConstPtr &cloud, const IndicesConstPtr &indices=IndicesConstPtr())
 Provide a pointer to the input dataset. More...
 
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. More...
 
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. More...
 

Protected Attributes

KdTreePtr tree_
 A pointer to the internal KdTree object. More...
 

Additional Inherited Members

Detailed Description

template<typename PointT, class Tree = pcl::KdTreeFLANN<PointT>>
class pcl::search::KdTree< PointT, Tree >

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 61 of file kdtree.h.

Member Typedef Documentation

template<typename PointT, class Tree = pcl::KdTreeFLANN<PointT>>
typedef boost::shared_ptr<const KdTree<PointT, Tree> > pcl::search::KdTree< PointT, Tree >::ConstPtr

Definition at line 79 of file kdtree.h.

template<typename PointT, class Tree = pcl::KdTreeFLANN<PointT>>
typedef boost::shared_ptr<const std::vector<int> > pcl::search::KdTree< PointT, Tree >::IndicesConstPtr

Definition at line 68 of file kdtree.h.

template<typename PointT, class Tree = pcl::KdTreeFLANN<PointT>>
typedef boost::shared_ptr<std::vector<int> > pcl::search::KdTree< PointT, Tree >::IndicesPtr

Definition at line 67 of file kdtree.h.

template<typename PointT, class Tree = pcl::KdTreeFLANN<PointT>>
typedef boost::shared_ptr<const Tree> pcl::search::KdTree< PointT, Tree >::KdTreeConstPtr

Definition at line 82 of file kdtree.h.

template<typename PointT, class Tree = pcl::KdTreeFLANN<PointT>>
typedef boost::shared_ptr<Tree> pcl::search::KdTree< PointT, Tree >::KdTreePtr

Definition at line 81 of file kdtree.h.

template<typename PointT, class Tree = pcl::KdTreeFLANN<PointT>>
typedef Search<PointT>::PointCloud pcl::search::KdTree< PointT, Tree >::PointCloud

Definition at line 64 of file kdtree.h.

template<typename PointT, class Tree = pcl::KdTreeFLANN<PointT>>
typedef Search<PointT>::PointCloudConstPtr pcl::search::KdTree< PointT, Tree >::PointCloudConstPtr

Definition at line 65 of file kdtree.h.

template<typename PointT, class Tree = pcl::KdTreeFLANN<PointT>>
typedef boost::shared_ptr<const PointRepresentation<PointT> > pcl::search::KdTree< PointT, Tree >::PointRepresentationConstPtr

Definition at line 83 of file kdtree.h.

template<typename PointT, class Tree = pcl::KdTreeFLANN<PointT>>
typedef boost::shared_ptr<KdTree<PointT, Tree> > pcl::search::KdTree< PointT, Tree >::Ptr

Definition at line 78 of file kdtree.h.

Constructor & Destructor Documentation

template<typename PointT , class Tree >
pcl::search::KdTree< PointT, Tree >::KdTree ( bool  sorted = true)

Constructor for KdTree.

Parameters
[in]sortedset 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 46 of file kdtree.hpp.

template<typename PointT, class Tree = pcl::KdTreeFLANN<PointT>>
virtual pcl::search::KdTree< PointT, Tree >::~KdTree ( )
inlinevirtual

Destructor for KdTree.

Definition at line 96 of file kdtree.h.

Member Function Documentation

template<typename PointT, class Tree = pcl::KdTreeFLANN<PointT>>
float pcl::search::KdTree< PointT, Tree >::getEpsilon ( ) const
inline

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

Definition at line 127 of file kdtree.h.

template<typename PointT, class Tree = pcl::KdTreeFLANN<PointT>>
PointRepresentationConstPtr pcl::search::KdTree< PointT, Tree >::getPointRepresentation ( ) const
inline

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

Definition at line 108 of file kdtree.h.

template<typename PointT, class Tree >
int pcl::search::KdTree< PointT, Tree >::nearestKSearch ( const PointT point,
int  k,
std::vector< int > &  k_indices,
std::vector< float > &  k_sqr_distances 
) const
virtual

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

Parameters
[in]pointthe given query point
[in]kthe number of neighbors to search for
[out]k_indicesthe resultant indices of the neighboring points (must be resized to k a priori!)
[out]k_sqr_distancesthe 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 88 of file kdtree.hpp.

References pcl::search::KdTree< PointT, Tree >::tree_.

Referenced by pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::computeCovariances(), pcl::search::KdTree< PointTarget >::getEpsilon(), pcl::getMeanPointDensity(), and pcl::SupervoxelClustering< PointT >::refineSupervoxels().

template<typename PointT, class Tree >
int pcl::search::KdTree< PointT, Tree >::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.

Parameters
[in]pointthe given query point
[in]radiusthe radius of the sphere bounding all of p_q's neighbors
[out]k_indicesthe resultant indices of the neighboring points
[out]k_sqr_distancesthe resultant squared distances to the neighboring points
[in]max_nnif 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 97 of file kdtree.hpp.

References pcl::search::KdTree< PointT, Tree >::tree_.

Referenced by pcl::search::KdTree< PointTarget >::getEpsilon(), pcl::HarrisKeypoint6D< PointInT, PointOutT, NormalT >::refineCorners(), and pcl::SupervoxelClustering< PointT >::refineSupervoxels().

template<typename PointT , class Tree >
void pcl::search::KdTree< PointT, Tree >::setEpsilon ( float  eps)

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

Parameters
[in]epsprecision (error bound) for nearest neighbors searches

Definition at line 70 of file kdtree.hpp.

References pcl::search::KdTree< PointT, Tree >::tree_.

Referenced by pcl::search::KdTree< PointTarget >::getPointRepresentation().

template<typename PointT , class Tree >
void pcl::search::KdTree< PointT, Tree >::setInputCloud ( const PointCloudConstPtr cloud,
const IndicesConstPtr indices = IndicesConstPtr () 
)
virtual
template<typename PointT , class Tree >
void pcl::search::KdTree< PointT, Tree >::setPointRepresentation ( const PointRepresentationConstPtr point_representation)

Provide a pointer to the point representation to use to convert points into k-D vectors.

Parameters
[in]point_representationthe const boost shared pointer to a PointRepresentation

Definition at line 54 of file kdtree.hpp.

References pcl::search::KdTree< PointT, Tree >::tree_.

Referenced by pcl::search::KdTree< PointTarget >::~KdTree().

template<typename PointT , class Tree >
void pcl::search::KdTree< PointT, Tree >::setSortedResults ( bool  sorted_results)
virtual

Sets whether the results have to be sorted or not.

Parameters
[in]sorted_resultsset to true if the radius search results should be sorted

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

Definition at line 62 of file kdtree.hpp.

References pcl::search::Search< PointT >::sorted_results_, and pcl::search::KdTree< PointT, Tree >::tree_.

Referenced by pcl::search::KdTree< PointTarget >::getPointRepresentation().

Member Data Documentation

template<typename PointT, class Tree = pcl::KdTreeFLANN<PointT>>
KdTreePtr pcl::search::KdTree< PointT, Tree >::tree_
protected

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