Point Cloud Library (PCL)  1.3.1
Public Types | Public Member Functions
pcl::KdTree Class Reference

KdTree represents the base spatial locator class for nearest neighbor estimation. More...

#include <pcl/kdtree/kdtree.h>

List of all members.

Public Types

typedef pcl::PointCloud< PointT > PointCloud
typedef boost::shared_ptr
< PointCloud
PointCloudPtr
typedef boost::shared_ptr
< const PointCloud
PointCloudConstPtr
typedef
pcl::PointRepresentation
< PointT > 
PointRepresentation
typedef boost::shared_ptr
< const PointRepresentation
PointRepresentationConstPtr
typedef boost::shared_ptr
< KdTree< PointT > > 
Ptr
typedef boost::shared_ptr
< const KdTree< PointT > > 
ConstPtr

Public Member Functions

 KdTree (bool sorted=true)
 Empty constructor for KdTree.
virtual void setInputCloud (const PointCloudConstPtr &cloud, const IndicesConstPtr &indices=IndicesConstPtr())
 Provide a pointer to the input dataset.
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.
virtual ~KdTree ()
 Destructor for KdTree.
virtual int nearestKSearch (const PointCloud &cloud, int index, int k, std::vector< int > &k_indices, std::vector< float > &k_sqr_distances)=0
 Search for k-nearest neighbors for the given query point.
virtual int nearestKSearch (const PointT &p_q, int k, std::vector< int > &k_indices, std::vector< float > &k_sqr_distances)=0
 Search for 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_distances)
 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)=0
 Search for k-nearest neighbors for the given query point (zero-copy).
virtual int radiusSearch (const PointCloud &cloud, int index, double radius, std::vector< int > &k_indices, std::vector< float > &k_sqr_distances, int max_nn=INT_MAX) const =0
 Search for all the nearest neighbors of the query point in a given radius.
virtual int radiusSearch (const PointT &p_q, double radius, std::vector< int > &k_indices, std::vector< float > &k_sqr_distances, int max_nn=INT_MAX) const =0
 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_distances, int max_nn=-1) 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, int max_nn=INT_MAX) const =0
 Search for all the nearest neighbors of the query point in a given radius (zero-copy).
void setEpsilon (double eps)
 Set the search epsilon precision (error bound) for nearest neighbors searches.
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

KdTree represents the base spatial locator class for nearest neighbor estimation.

All types of spatial locators should inherit from KdTree.

Author:
Radu Bogdan Rusu, Bastian Steder, Michael Dixon

Member Typedef Documentation

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

Reimplemented in pcl::KdTreeFLANN.

Definition at line 71 of file kdtree.h.

Definition at line 61 of file kdtree.h.

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

Definition at line 63 of file kdtree.h.

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

Definition at line 62 of file kdtree.h.

Definition at line 65 of file kdtree.h.

Definition at line 67 of file kdtree.h.

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

Reimplemented in pcl::KdTreeFLANN.

Definition at line 70 of file kdtree.h.


Constructor & Destructor Documentation

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

Empty constructor for KdTree.

Sets some internal values to their defaults.

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

Definition at line 76 of file kdtree.h.

virtual pcl::KdTree::~KdTree ( ) [inline, virtual]

Destructor for KdTree.

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

Definition at line 126 of file kdtree.h.


Member Function Documentation

double pcl::KdTree::getEpsilon ( ) [inline]

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

Definition at line 258 of file kdtree.h.

IndicesConstPtr const pcl::KdTree::getIndices ( ) [inline]

Get a pointer to the vector of indices used.

Definition at line 96 of file kdtree.h.

PointCloudConstPtr pcl::KdTree::getInputCloud ( ) [inline]

Get a pointer to the input point cloud dataset.

Definition at line 103 of file kdtree.h.

float pcl::KdTree::getMinPts ( ) [inline]

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

Definition at line 274 of file kdtree.h.

PointRepresentationConstPtr const pcl::KdTree::getPointRepresentation ( ) [inline]

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

Definition at line 120 of file kdtree.h.

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

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

Parameters:
[in]cloudthe point cloud data
[in]indexthe index in cloud representing the 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_distancesthe resultant squared distances to the neighboring points (must be resized to k a priori!)
Returns:
number of neighbors found
virtual int pcl::KdTree::nearestKSearch ( const PointT &  p_q,
int  k,
std::vector< int > &  k_indices,
std::vector< float > &  k_sqr_distances 
) [pure virtual]

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

Parameters:
[in]p_qthe given query point
in[k the number of neighbors to search for
[out]k_indicesthe resultant indices of the neighboring points (must be resized to k a priori!)
[out]k_distancesthe resultant squared distances to the neighboring points (must be resized to k a priori!)
Returns:
number of neighbors found

Implemented in pcl::KdTreeFLANN.

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

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

Parameters:
indexthe 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
kthe number of neighbors to search for
k_indicesthe resultant indices of the neighboring points (must be resized to k a priori!)
k_sqr_distancesthe resultant squared distances to the neighboring points (must be resized to k a priori!)
Returns:
number of neighbors found

Implemented in pcl::KdTreeFLANN.

template<typename PointTDiff >
int pcl::KdTree::nearestKSearchT ( const PointTDiff &  point,
int  k,
std::vector< int > &  k_indices,
std::vector< float > &  k_distances 
) [inline]

Search for 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_distancesthe resultant squared distances to the neighboring points (must be resized to k a priori!)
Returns:
number of neighbors found

Definition at line 162 of file kdtree.h.

virtual int pcl::KdTree::radiusSearch ( const PointCloud cloud,
int  index,
double  radius,
std::vector< int > &  k_indices,
std::vector< float > &  k_sqr_distances,
int  max_nn = INT_MAX 
) const [pure virtual]

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

Parameters:
[in]cloudthe point cloud data
[in]indexthe index in cloud representing the 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_distancesthe resultant squared distances to the neighboring points
[in]max_nnif given, bounds the maximum returned neighbors to this value
Returns:
number of neighbors found in radius
virtual int pcl::KdTree::radiusSearch ( const PointT &  p_q,
double  radius,
std::vector< int > &  k_indices,
std::vector< float > &  k_sqr_distances,
int  max_nn = INT_MAX 
) const [pure virtual]

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

Parameters:
[in]p_qthe 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_distancesthe resultant squared distances to the neighboring points
[in]max_nnif given, bounds the maximum returned neighbors to this value
Returns:
number of neighbors found in radius

Implemented in pcl::KdTreeFLANN.

virtual int pcl::KdTree::radiusSearch ( int  index,
double  radius,
std::vector< int > &  k_indices,
std::vector< float > &  k_sqr_distances,
int  max_nn = INT_MAX 
) const [pure virtual]

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

Parameters:
[in]indexthe 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]radiusthe radius of the sphere bounding all of p_q's neighbors
[out]k_indicesthe resultant indices of the neighboring points
[out]k_distancesthe resultant squared distances to the neighboring points
[in]max_nnif given, bounds the maximum returned neighbors to this value
Returns:
number of neighbors found in radius

Implemented in pcl::KdTreeFLANN.

template<typename PointTDiff >
int pcl::KdTree::radiusSearchT ( const PointTDiff &  point,
double  radius,
std::vector< int > &  k_indices,
std::vector< float > &  k_distances,
int  max_nn = -1 
) const [inline]

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_distancesthe resultant squared distances to the neighboring points
[in]max_nnif given, bounds the maximum returned neighbors to this value
Returns:
number of neighbors found in radius

Definition at line 221 of file kdtree.h.

void pcl::KdTree::setEpsilon ( double  eps) [inline]

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

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

Definition at line 251 of file kdtree.h.

virtual void pcl::KdTree::setInputCloud ( const PointCloudConstPtr cloud,
const IndicesConstPtr &  indices = IndicesConstPtr () 
) [inline, virtual]

Provide a pointer to the input dataset.

Parameters:
[in]cloudthe const boost shared pointer to a PointCloud message
[in]indicesthe point indices subset that is to be used from cloud - if NULL the whole cloud is used

Definition at line 88 of file kdtree.h.

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

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

Parameters:
[in]min_ptsthe minimum number of neighbors in a viable neighborhood

Definition at line 267 of file kdtree.h.

void pcl::KdTree::setPointRepresentation ( const PointRepresentationConstPtr point_representation) [inline]

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 112 of file kdtree.h.


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