Public Types | Public Member Functions | Protected Member Functions | Protected Attributes

pcl::KdTree< PointT > Class Template Reference
[Module kdtree]

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

#include <pcl/kdtree/kdtree.h>

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

Protected Member Functions

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

Protected Attributes

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

Detailed Description

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

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

Definition at line 54 of file kdtree.h.


Member Typedef Documentation

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

Definition at line 60 of file kdtree.h.

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

Definition at line 62 of file kdtree.h.

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

Definition at line 61 of file kdtree.h.

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

Definition at line 64 of file kdtree.h.

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

Definition at line 66 of file kdtree.h.

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

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.

Definition at line 73 of file kdtree.h.

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

Destructor for KdTree.

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

Definition at line 123 of file kdtree.h.


Member Function Documentation

template<typename PointT>
double pcl::KdTree< PointT >::getEpsilon (  )  [inline]

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

Definition at line 209 of file kdtree.h.

template<typename PointT>
IndicesConstPtr const pcl::KdTree< PointT >::getIndices (  )  [inline]

Get a pointer to the vector of indices used.

Definition at line 93 of file kdtree.h.

template<typename PointT>
PointCloudConstPtr pcl::KdTree< PointT >::getInputCloud (  )  [inline]

Get a pointer to the input point cloud dataset.

Definition at line 100 of file kdtree.h.

template<typename PointT>
float pcl::KdTree< PointT >::getMinPts (  )  [inline]

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

Definition at line 223 of file kdtree.h.

template<typename PointT>
virtual std::string pcl::KdTree< PointT >::getName (  )  const [protected, pure virtual]

Class getName method.

template<typename PointT>
PointRepresentationConstPtr const pcl::KdTree< PointT >::getPointRepresentation (  )  [inline]

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

Definition at line 117 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 
) [pure virtual]

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

Parameters:
index the 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
k the number of neighbors to search for
k_indices the resultant indices of the neighboring points (must be resized to k a priori!)
k_sqr_distances the resultant squared distances to the neighboring points (must be resized to k a priori!)
Returns:
number of neighbors found

Implemented in pcl::KdTreeFLANN< PointT >, pcl::OrganizedDataIndex< PointT >, and pcl::KdTreeFLANN< FeatureT >.

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 
) [pure virtual]

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

Parameters:
cloud the point cloud data
index the index in cloud representing the query point
k the number of neighbors to search for
k_indices the resultant indices of the neighboring points (must be resized to k a priori!)
k_sqr_distances the resultant squared distances to the neighboring points (must be resized to k a priori!)
Returns:
number of neighbors found

Implemented in pcl::KdTreeFLANN< PointT >, pcl::OrganizedDataIndex< PointT >, and pcl::KdTreeFLANN< FeatureT >.

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 
) [pure virtual]

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

Parameters:
p_q the given query point
k the number of neighbors to search for
k_indices the resultant indices of the neighboring points (must be resized to k a priori!)
k_sqr_distances the resultant squared distances to the neighboring points (must be resized to k a priori!)
Returns:
number of neighbors found

Implemented in pcl::KdTreeFLANN< PointT >, pcl::OrganizedDataIndex< PointT >, and pcl::KdTreeFLANN< FeatureT >.

template<typename PointT>
virtual int pcl::KdTree< PointT >::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:
index the 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
radius the radius of the sphere bounding all of p_q's neighbors
k_indices the resultant indices of the neighboring points
k_sqr_distances the resultant squared distances to the neighboring points
max_nn if given, bounds the maximum returned neighbors to this value
Returns:
number of neighbors found in radius

Implemented in pcl::KdTreeFLANN< PointT >, pcl::OrganizedDataIndex< PointT >, and pcl::KdTreeFLANN< FeatureT >.

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,
int  max_nn = INT_MAX 
) const [pure virtual]

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

Parameters:
cloud the point cloud data
index the index in cloud representing the query point
radius the radius of the sphere bounding all of p_q's neighbors
k_indices the resultant indices of the neighboring points
k_sqr_distances the resultant squared distances to the neighboring points
max_nn if given, bounds the maximum returned neighbors to this value
Returns:
number of neighbors found in radius

Implemented in pcl::KdTreeFLANN< PointT >, pcl::OrganizedDataIndex< PointT >, and pcl::KdTreeFLANN< FeatureT >.

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,
int  max_nn = INT_MAX 
) const [pure virtual]

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

Parameters:
p_q the given query point
radius the radius of the sphere bounding all of p_q's neighbors
k_indices the resultant indices of the neighboring points
k_sqr_distances the resultant squared distances to the neighboring points
max_nn if given, bounds the maximum returned neighbors to this value
Returns:
number of neighbors found in radius

Implemented in pcl::KdTreeFLANN< PointT >, pcl::OrganizedDataIndex< PointT >, and pcl::KdTreeFLANN< FeatureT >.

template<typename PointT>
void pcl::KdTree< PointT >::setEpsilon ( double  eps  )  [inline]

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

Parameters:
eps precision (error bound) for nearest neighbors searches

Definition at line 202 of file kdtree.h.

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

Provide a pointer to the input dataset.

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

Reimplemented in pcl::KdTreeFLANN< PointT >, and pcl::KdTreeFLANN< FeatureT >.

Definition at line 85 of file kdtree.h.

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.

Definition at line 216 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:
point_representation the const boost shared pointer to a PointRepresentation

Definition at line 109 of file kdtree.h.


Member Data Documentation

template<typename PointT>
double pcl::KdTree< PointT >::epsilon_ [protected]

Epsilon precision (error bound) for nearest neighbors searches.

Definition at line 236 of file kdtree.h.

template<typename PointT>
IndicesConstPtr pcl::KdTree< PointT >::indices_ [protected]

A pointer to the vector of point indices to use.

Definition at line 233 of file kdtree.h.

template<typename PointT>
PointCloudConstPtr pcl::KdTree< PointT >::input_ [protected]

The input point cloud dataset containing the points we need to use.

Definition at line 230 of file kdtree.h.

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 239 of file kdtree.h.

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 245 of file kdtree.h.

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

Return the radius search neighbours sorted.

Definition at line 242 of file kdtree.h.


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