Point Cloud Library (PCL)  1.8.0-dev
List of all members | Public Types | Public Member Functions | Protected Member Functions | Protected Attributes
pcl::KdTree< PointT > Class Template Referenceabstract

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

#include <pcl/kdtree/kdtree.h>

+ Inheritance diagram for pcl::KdTree< PointT >:

Public Types

typedef boost::shared_ptr
< std::vector< int > > 
IndicesPtr
 
typedef boost::shared_ptr
< const std::vector< int > > 
IndicesConstPtr
 
typedef pcl::PointCloud< PointTPointCloud
 
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. More...
 
virtual void setInputCloud (const PointCloudConstPtr &cloud, const IndicesConstPtr &indices=IndicesConstPtr())
 Provide a pointer to the input dataset. More...
 
IndicesConstPtr getIndices () const
 Get a pointer to the vector of indices used. More...
 
PointCloudConstPtr getInputCloud () const
 Get a pointer to the input point cloud dataset. 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...
 
virtual ~KdTree ()
 Destructor for KdTree. More...
 
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. More...
 
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. More...
 
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. More...
 
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). More...
 
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. More...
 
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. More...
 
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. More...
 
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). More...
 
virtual 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 setMinPts (int min_pts)
 Minimum allowed number of k nearest neighbors points that a viable result must contain. More...
 
int getMinPts () const
 Get the minimum allowed number of k nearest neighbors points that a viable result must contain. More...
 

Protected Member Functions

virtual std::string getName () const =0
 Class getName method. More...
 

Protected Attributes

PointCloudConstPtr input_
 The input point cloud dataset containing the points we need to use. More...
 
IndicesConstPtr indices_
 A pointer to the vector of point indices to use. More...
 
float epsilon_
 Epsilon precision (error bound) for nearest neighbors searches. More...
 
int min_pts_
 Minimum allowed number of k nearest neighbors points that a viable result must contain. More...
 
bool sorted_
 Return the radius search neighbours sorted. More...
 
PointRepresentationConstPtr point_representation_
 For converting different point structures into k-dimensional vectors for nearest-neighbor search. More...
 

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.h.

Member Typedef Documentation

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

Definition at line 72 of file kdtree.h.

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

Definition at line 60 of file kdtree.h.

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

Definition at line 59 of file kdtree.h.

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

Definition at line 62 of file kdtree.h.

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

Definition at line 64 of file kdtree.h.

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

Definition at line 63 of file kdtree.h.

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

Definition at line 66 of file kdtree.h.

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

Definition at line 68 of file kdtree.h.

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

Definition at line 71 of file 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]sortedset 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.h.

template<typename PointT>
virtual pcl::KdTree< PointT >::~KdTree ( )
inlinevirtual

Destructor for KdTree.

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

Definition at line 127 of file 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 321 of file 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.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.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 337 of file kdtree.h.

template<typename PointT>
virtual std::string pcl::KdTree< PointT >::getName ( ) const
protectedpure virtual

Class getName method.

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 121 of file 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_qthe 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

Implemented in pcl::KdTreeFLANN< PointT, Dist >, pcl::KdTreeFLANN< pcl::pcl::InterestPoint >, pcl::KdTreeFLANN< PointT >, pcl::KdTreeFLANN< pcl::PointXYZRGB >, pcl::KdTreeFLANN< pcl::pcl::PointXYZ >, pcl::KdTreeFLANN< pcl::VFHSignature308 >, pcl::KdTreeFLANN< PointTarget >, and pcl::KdTreeFLANN< FeatureT >.

Referenced by pcl::KdTree< FeatureT >::nearestKSearch(), and pcl::KdTree< FeatureT >::nearestKSearchT().

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
inlinevirtual

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]cloudthe point cloud data
[in]indexa valid index in cloud representing a valid (i.e., finite) 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
Exceptions
assertsin debug mode if the index is not between 0 and the maximum number of points

Definition at line 158 of file 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
inlinevirtual

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]indexa 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]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
Exceptions
assertsin debug mode if the index is not between 0 and the maximum number of points

Definition at line 201 of file 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]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

Definition at line 175 of file kdtree.h.

Referenced by pcl::getApproximateIndices().

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_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_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

Implemented in pcl::KdTreeFLANN< PointT, Dist >, pcl::KdTreeFLANN< pcl::pcl::InterestPoint >, pcl::KdTreeFLANN< PointT >, pcl::KdTreeFLANN< pcl::PointXYZRGB >, pcl::KdTreeFLANN< pcl::pcl::PointXYZ >, pcl::KdTreeFLANN< pcl::VFHSignature308 >, pcl::KdTreeFLANN< PointTarget >, and pcl::KdTreeFLANN< FeatureT >.

Referenced by pcl::KdTree< FeatureT >::radiusSearch(), and pcl::KdTree< FeatureT >::radiusSearchT().

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
inlinevirtual

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]cloudthe point cloud data
[in]indexa valid index in cloud representing a valid (i.e., finite) 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
Exceptions
assertsin debug mode if the index is not between 0 and the maximum number of points

Definition at line 248 of file 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
inlinevirtual

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]indexa 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]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
Exceptions
assertsin debug mode if the index is not between 0 and the maximum number of points

Definition at line 295 of file 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]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

Definition at line 267 of file kdtree.h.

template<typename PointT>
virtual void pcl::KdTree< PointT >::setEpsilon ( float  eps)
inlinevirtual

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

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

Reimplemented in pcl::KdTreeFLANN< PointT, Dist >, pcl::KdTreeFLANN< pcl::pcl::InterestPoint >, pcl::KdTreeFLANN< PointT >, pcl::KdTreeFLANN< pcl::PointXYZRGB >, pcl::KdTreeFLANN< pcl::pcl::PointXYZ >, pcl::KdTreeFLANN< pcl::VFHSignature308 >, pcl::KdTreeFLANN< PointTarget >, and pcl::KdTreeFLANN< FeatureT >.

Definition at line 314 of file kdtree.h.

template<typename PointT>
virtual void pcl::KdTree< PointT >::setInputCloud ( const PointCloudConstPtr cloud,
const IndicesConstPtr indices = IndicesConstPtr () 
)
inlinevirtual

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

Reimplemented in pcl::KdTreeFLANN< PointT, Dist >, pcl::KdTreeFLANN< pcl::pcl::InterestPoint >, pcl::KdTreeFLANN< PointT >, pcl::KdTreeFLANN< pcl::PointXYZRGB >, pcl::KdTreeFLANN< pcl::pcl::PointXYZ >, pcl::KdTreeFLANN< pcl::VFHSignature308 >, pcl::KdTreeFLANN< PointTarget >, and pcl::KdTreeFLANN< FeatureT >.

Definition at line 88 of file kdtree.h.

Referenced by pcl::KdTree< FeatureT >::setPointRepresentation().

template<typename PointT>
void pcl::KdTree< PointT >::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 330 of file kdtree.h.

template<typename PointT>
void pcl::KdTree< PointT >::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.

Member Data Documentation

template<typename PointT>
float pcl::KdTree< PointT >::epsilon_
protected

Epsilon precision (error bound) for nearest neighbors searches.

Definition at line 350 of file kdtree.h.

Referenced by pcl::KdTree< FeatureT >::getEpsilon(), and pcl::KdTree< FeatureT >::setEpsilon().

template<typename PointT>
IndicesConstPtr pcl::KdTree< PointT >::indices_
protected
template<typename PointT>
PointCloudConstPtr pcl::KdTree< PointT >::input_
protected
template<typename PointT>
int pcl::KdTree< PointT >::min_pts_
protected

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

Definition at line 353 of file kdtree.h.

Referenced by pcl::KdTree< FeatureT >::getMinPts(), and pcl::KdTree< FeatureT >::setMinPts().

template<typename PointT>
PointRepresentationConstPtr pcl::KdTree< PointT >::point_representation_
protected

For converting different point structures into k-dimensional vectors for nearest-neighbor search.

Definition at line 359 of file kdtree.h.

Referenced by pcl::KdTree< FeatureT >::getPointRepresentation(), and pcl::KdTree< FeatureT >::setPointRepresentation().

template<typename PointT>
bool pcl::KdTree< PointT >::sorted_
protected

Return the radius search neighbours sorted.

Definition at line 356 of file kdtree.h.


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