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

KdTreeFLANN is a generic type of 3D spatial locator using kD-tree structures. More...

#include <pcl/kdtree/kdtree_flann.h>

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List of all members.

Public Types

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 (KdTreeFLANN &tree)
 Copy constructor.
Ptr makeShared ()
KdTreeFLANNoperator= (const KdTreeFLANN &tree)
void shallowCopy (const KdTreeFLANN &tree)
 Perform a shallow copy of the tree.
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_distances)
 Search for k-nearest neighbors for the given query point.
int nearestKSearch (const PointCloud &cloud, int index, int k, std::vector< int > &k_indices, std::vector< float > &k_distances)
 Search for k-nearest neighbors for the given query point.
int nearestKSearch (int index, int k, std::vector< int > &k_indices, std::vector< float > &k_distances)
 Search for k-nearest neighbors for the given query point (zero-copy).
int radiusSearch (const PointT &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.
int radiusSearch (const PointCloud &cloud, int index, 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.
int radiusSearch (int index, 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 (zero-copy).
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 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.
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 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.
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.
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

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 Bogdan Rusu, Marius Muja

Member Typedef Documentation

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

Reimplemented from pcl::KdTree< PointT >.

Definition at line 84 of file kdtree_flann.h.

typedef boost::shared_ptr<PointCloud> pcl::KdTree::PointCloudPtr [inherited]

Definition at line 62 of file kdtree.h.

Definition at line 65 of file kdtree.h.

typedef boost::shared_ptr<const PointRepresentation> pcl::KdTree::PointRepresentationConstPtr [inherited]

Definition at line 67 of file kdtree.h.

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

Reimplemented from pcl::KdTree< PointT >.

Definition at line 83 of file kdtree_flann.h.


Constructor & Destructor Documentation

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

Default Constructor for KdTreeFLANN.

Parameters:
[in]sortedset 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 91 of file kdtree_flann.h.

pcl::KdTreeFLANN::KdTreeFLANN ( KdTreeFLANN tree) [inline]

Copy constructor.

This copy constructor does shallow copy of the tree, the only reason why it's needed is because boost::mutex is non-copyable, so the default copy constructor would not work

Parameters:
[in]treethe tree to copy

Definition at line 104 of file kdtree_flann.h.

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

Destructor for KdTreeFLANN.

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

Definition at line 138 of file kdtree_flann.h.


Member Function Documentation

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

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, inherited]

Get a pointer to the vector of indices used.

Definition at line 96 of file kdtree.h.

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

Get a pointer to the input point cloud dataset.

Definition at line 103 of file kdtree.h.

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

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, inherited]

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

Definition at line 120 of file kdtree.h.

Ptr pcl::KdTreeFLANN::makeShared ( ) [inline]

Definition at line 109 of file kdtree_flann.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, inherited]

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
int pcl::KdTreeFLANN::nearestKSearch ( const PointT &  point,
int  k,
std::vector< int > &  k_indices,
std::vector< float > &  k_distances 
) [virtual]

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

Parameters:
[in]pointthe 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

Implements pcl::KdTree< PointT >.

Definition at line 76 of file kdtree_flann.hpp.

int pcl::KdTreeFLANN::nearestKSearch ( const PointCloud &  cloud,
int  index,
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]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

Definition at line 172 of file kdtree_flann.h.

int pcl::KdTreeFLANN::nearestKSearch ( int  index,
int  k,
std::vector< int > &  k_indices,
std::vector< float > &  k_distances 
) [inline, virtual]

Search for k-nearest neighbors for the given query point (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]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

Implements pcl::KdTree< PointT >.

Definition at line 190 of file kdtree_flann.h.

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

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.

KdTreeFLANN& pcl::KdTreeFLANN::operator= ( const KdTreeFLANN tree) [inline]

Definition at line 111 of file kdtree_flann.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, inherited]

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
int pcl::KdTreeFLANN::radiusSearch ( const PointT &  point,
double  radius,
std::vector< int > &  k_indices,
std::vector< float > &  k_distances,
int  max_nn = -1 
) 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_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

Implements pcl::KdTree< PointT >.

Definition at line 113 of file kdtree_flann.hpp.

int pcl::KdTreeFLANN::radiusSearch ( const PointCloud &  cloud,
int  index,
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]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

Definition at line 229 of file kdtree_flann.h.

int pcl::KdTreeFLANN::radiusSearch ( int  index,
double  radius,
std::vector< int > &  k_indices,
std::vector< float > &  k_distances,
int  max_nn = -1 
) const [inline, 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

Implements pcl::KdTree< PointT >.

Definition at line 248 of file kdtree_flann.h.

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, inherited]

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, inherited]

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, inherited]

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::KdTreeFLANN::setInputCloud ( const PointCloudConstPtr &  cloud,
const IndicesConstPtr &  indices = IndicesConstPtr () 
)

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 45 of file kdtree_flann.hpp.

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

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, inherited]

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.

void pcl::KdTreeFLANN::shallowCopy ( const KdTreeFLANN tree) [inline]

Perform a shallow copy of the tree.

Parameters:
[in]treethe tree to copy

Definition at line 125 of file kdtree_flann.h.


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