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

#include <pcl/kdtree/kdtree.h>

Public Types

using IndicesPtr = shared_ptr< Indices >
using IndicesConstPtr = shared_ptr< const Indices >
using PointCloud = pcl::PointCloud< PointT >
using PointCloudPtr = typename PointCloud::Ptr
using PointCloudConstPtr = typename PointCloud::ConstPtr
using PointRepresentation = pcl::PointRepresentation< PointT >
using PointRepresentationConstPtr = typename PointRepresentation::ConstPtr
using Ptr = shared_ptr< KdTree< PointT > >
using ConstPtr = shared_ptr< const KdTree< PointT > >

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, unsigned int k, Indices &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, unsigned int k, Indices &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, unsigned int k, Indices &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, unsigned int k, Indices &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, Indices &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, Indices &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, Indices &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, Indices &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 54 of file kdtree.h.

Member Typedef Documentation

ConstPtr

template<typename PointT >
using pcl::KdTree< PointT >::ConstPtr = shared_ptr<const KdTree<PointT> >

Definition at line 69 of file kdtree.h.

IndicesConstPtr

template<typename PointT >
using pcl::KdTree< PointT >::IndicesConstPtr = shared_ptr<const Indices >

Definition at line 58 of file kdtree.h.

IndicesPtr

template<typename PointT >
using pcl::KdTree< PointT >::IndicesPtr = shared_ptr<Indices >

Definition at line 57 of file kdtree.h.

PointCloud

template<typename PointT >
using pcl::KdTree< PointT >::PointCloud = pcl::PointCloud<PointT>

Definition at line 60 of file kdtree.h.

PointCloudConstPtr

template<typename PointT >
using pcl::KdTree< PointT >::PointCloudConstPtr = typename PointCloud::ConstPtr

Definition at line 62 of file kdtree.h.

PointCloudPtr

template<typename PointT >
using pcl::KdTree< PointT >::PointCloudPtr = typename PointCloud::Ptr

Definition at line 61 of file kdtree.h.

PointRepresentation

template<typename PointT >
using pcl::KdTree< PointT >::PointRepresentation = pcl::PointRepresentation<PointT>

Definition at line 64 of file kdtree.h.

PointRepresentationConstPtr

template<typename PointT >
using pcl::KdTree< PointT >::PointRepresentationConstPtr = typename PointRepresentation::ConstPtr

Definition at line 65 of file kdtree.h.

Ptr

template<typename PointT >
using pcl::KdTree< PointT >::Ptr = shared_ptr<KdTree<PointT> >

Definition at line 68 of file kdtree.h.

Constructor & Destructor Documentation

KdTree()

template<typename PointT >
pcl::KdTree< PointT >::KdTree ( bool sorted = true )
inline

Empty constructor for KdTree.

Sets some internal values to their defaults.

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

Definition at line 74 of file kdtree.h.

~KdTree()

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 124 of file kdtree.h.

Member Function Documentation

getEpsilon()

template<typename PointT >
float pcl::KdTree< PointT >::getEpsilon ( ) const
inline

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

Definition at line 313 of file kdtree.h.

getIndices()

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

Get a pointer to the vector of indices used.

Definition at line 93 of file kdtree.h.

Referenced by pcl::extractEuclideanClusters().

getInputCloud()

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

Get a pointer to the input point cloud dataset.

Definition at line 100 of file kdtree.h.

Referenced by pcl::extractEuclideanClusters().

getMinPts()

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 329 of file kdtree.h.

getName()

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

Class getName method.

getPointRepresentation()

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 118 of file kdtree.h.

nearestKSearch() [1/3]

template<typename PointT >
virtual int pcl::KdTree< PointT >::nearestKSearch ( const PointCloud & cloud,
int index,
unsigned int k,
Indices & 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.size () || index < 0), and assumes valid (i.e., finite) data.
Parameters
[in] cloud the point cloud data
[in] index a valid index in cloud representing a valid (i.e., finite) query point
[in] k the number of neighbors to search for
[out] k_indices the resultant indices of the neighboring points (must be resized to k a priori!)
[out] k_sqr_distances the resultant squared distances to the neighboring points (must be resized to k a priori!)
Returns
number of neighbors found
Exceptions
asserts in debug mode if the index is not between 0 and the maximum number of points

Definition at line 155 of file kdtree.h.

nearestKSearch() [2/3]

template<typename PointT >
virtual int pcl::KdTree< PointT >::nearestKSearch ( const PointT & p_q,
unsigned int k,
Indices & k_indices,
std::vector< float > & k_sqr_distances
) const
pure virtual

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

Parameters
[in] p_q the given query point
[in] k the number of neighbors to search for
[out] k_indices the resultant indices of the neighboring points (must be resized to k a priori!)
[out] 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< SceneT >, pcl::KdTreeFLANN< PointTarget >, pcl::KdTreeFLANN< PointT, Dist >, pcl::KdTreeFLANN< PointT >, pcl::KdTreeFLANN< pcl::VFHSignature308 >, pcl::KdTreeFLANN< pcl::PointXYZRGB >, pcl::KdTreeFLANN< pcl::PointXYZLAB >, pcl::KdTreeFLANN< pcl::InterestPoint >, and pcl::KdTreeFLANN< FeatureT >.

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

nearestKSearch() [3/3]

template<typename PointT >
virtual int pcl::KdTree< PointT >::nearestKSearch ( int index,
unsigned int k,
Indices & 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.size () || index < 0), and assumes valid (i.e., finite) data.
Parameters
[in] index a 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] k the number of neighbors to search for
[out] k_indices the resultant indices of the neighboring points (must be resized to k a priori!)
[out] k_sqr_distances the resultant squared distances to the neighboring points (must be resized to k a priori!)
Returns
number of neighbors found
Exceptions
asserts in debug mode if the index is not between 0 and the maximum number of points

Definition at line 198 of file kdtree.h.

nearestKSearchT()

template<typename PointT >
template<typename PointTDiff >
int pcl::KdTree< PointT >::nearestKSearchT ( const PointTDiff & point,
unsigned int k,
Indices & 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] point the given query point
[in] k the number of neighbors to search for
[out] k_indices the resultant indices of the neighboring points (must be resized to k a priori!)
[out] k_sqr_distances the 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.h.

Referenced by pcl::getApproximateIndices().

radiusSearch() [1/3]

template<typename PointT >
virtual int pcl::KdTree< PointT >::radiusSearch ( const PointCloud & cloud,
int index,
double radius,
Indices & 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.size () || index < 0), and assumes valid (i.e., finite) data.
Parameters
[in] cloud the point cloud data
[in] index a valid index in cloud representing a valid (i.e., finite) query point
[in] radius the radius of the sphere bounding all of p_q's neighbors
[out] k_indices the resultant indices of the neighboring points
[out] k_sqr_distances the resultant squared distances to the neighboring points
[in] max_nn if 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
asserts in debug mode if the index is not between 0 and the maximum number of points

Definition at line 243 of file kdtree.h.

radiusSearch() [2/3]

template<typename PointT >
virtual int pcl::KdTree< PointT >::radiusSearch ( const PointT & p_q,
double radius,
Indices & 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_q the given query point
[in] radius the radius of the sphere bounding all of p_q's neighbors
[out] k_indices the resultant indices of the neighboring points
[out] k_sqr_distances the resultant squared distances to the neighboring points
[in] max_nn if 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< SceneT >, pcl::KdTreeFLANN< PointTarget >, pcl::KdTreeFLANN< PointT, Dist >, pcl::KdTreeFLANN< PointT >, pcl::KdTreeFLANN< pcl::VFHSignature308 >, pcl::KdTreeFLANN< pcl::PointXYZRGB >, pcl::KdTreeFLANN< pcl::PointXYZLAB >, pcl::KdTreeFLANN< pcl::InterestPoint >, and pcl::KdTreeFLANN< FeatureT >.

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

radiusSearch() [3/3]

template<typename PointT >
virtual int pcl::KdTree< PointT >::radiusSearch ( int index,
double radius,
Indices & 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.size () || index < 0), and assumes valid (i.e., finite) data.
Parameters
[in] index a 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] radius the radius of the sphere bounding all of p_q's neighbors
[out] k_indices the resultant indices of the neighboring points
[out] k_sqr_distances the resultant squared distances to the neighboring points
[in] max_nn if 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
asserts in debug mode if the index is not between 0 and the maximum number of points

Definition at line 290 of file kdtree.h.

radiusSearchT()

template<typename PointT >
template<typename PointTDiff >
int pcl::KdTree< PointT >::radiusSearchT ( const PointTDiff & point,
double radius,
Indices & 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] point the given query point
[in] radius the radius of the sphere bounding all of p_q's neighbors
[out] k_indices the resultant indices of the neighboring points
[out] k_sqr_distances the resultant squared distances to the neighboring points
[in] max_nn if 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 262 of file kdtree.h.

setEpsilon()

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] eps precision (error bound) for nearest neighbors searches

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

Definition at line 306 of file kdtree.h.

setInputCloud()

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] cloud the const boost shared pointer to a PointCloud message
[in] indices the 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< PointT >, pcl::KdTreeFLANN< pcl::PointXYZRGB >, pcl::KdTreeFLANN< pcl::VFHSignature308 >, pcl::KdTreeFLANN< SceneT >, pcl::KdTreeFLANN< PointTarget >, pcl::KdTreeFLANN< pcl::PointXYZLAB >, pcl::KdTreeFLANN< pcl::InterestPoint >, and pcl::KdTreeFLANN< FeatureT >.

Definition at line 85 of file kdtree.h.

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

setMinPts()

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_pts the minimum number of neighbors in a viable neighborhood

Definition at line 322 of file kdtree.h.

setPointRepresentation()

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_representation the const boost shared pointer to a PointRepresentation

Definition at line 109 of file kdtree.h.

Member Data Documentation

epsilon_

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

Epsilon precision (error bound) for nearest neighbors searches.

Definition at line 342 of file kdtree.h.

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

indices_

input_

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

min_pts_

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 345 of file kdtree.h.

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

point_representation_

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 351 of file kdtree.h.

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

sorted_

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

Return the radius search neighbours sorted.

Definition at line 348 of file kdtree.h.


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

© 2009–2012, Willow Garage, Inc.
© 2012–, Open Perception, Inc.
Licensed under the BSD License.
https://pointclouds.org/documentation/classpcl_1_1_kd_tree.html