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

#include <pcl/kdtree/kdtree_flann.h>

Public Types

using PointCloud = typename KdTree< PointT >::PointCloud
using PointCloudConstPtr = typename KdTree< PointT >::PointCloudConstPtr
using IndicesPtr = shared_ptr< Indices >
using IndicesConstPtr = shared_ptr< const Indices >
using FLANNIndex = ::flann::Index< Dist >
using Ptr = shared_ptr< KdTreeFLANN< PointT, Dist > >
using ConstPtr = shared_ptr< const KdTreeFLANN< PointT, Dist > >
- Public Types inherited from pcl::KdTree< PointT >
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

KdTreeFLANN (bool sorted=true)
Default Constructor for KdTreeFLANN. More...
KdTreeFLANN (const KdTreeFLANN< PointT, Dist > &k)
Copy constructor. More...
KdTreeFLANN< PointT, Dist > & operator= (const KdTreeFLANN< PointT, Dist > &k)
Copy operator. More...
void setEpsilon (float eps) override
Set the search epsilon precision (error bound) for nearest neighbors searches. More...
void setSortedResults (bool sorted)
Ptr makeShared ()
~KdTreeFLANN ()
Destructor for KdTreeFLANN. More...
void setInputCloud (const PointCloudConstPtr &cloud, const IndicesConstPtr &indices=IndicesConstPtr()) override
Provide a pointer to the input dataset. More...
int nearestKSearch (const PointT &point, unsigned int k, Indices &k_indices, std::vector< float > &k_sqr_distances) const override
Search for k-nearest neighbors for the given query point. More...
int radiusSearch (const PointT &point, double radius, Indices &k_indices, std::vector< float > &k_sqr_distances, unsigned int max_nn=0) const override
Search for all the nearest neighbors of the query point in a given radius. More...
- Public Member Functions inherited from pcl::KdTree< PointT >
KdTree (bool sorted=true)
Empty constructor for KdTree. 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 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 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...
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...

Additional Inherited Members

- Protected Attributes inherited from pcl::KdTree< PointT >
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, typename Dist = ::flann::L2_Simple<float>>
class pcl::KdTreeFLANN< PointT, Dist >

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 B. Rusu, Marius Muja

Definition at line 132 of file kdtree_flann.h.

Member Typedef Documentation

ConstPtr

template<typename PointT , typename Dist = ::flann::L2_Simple<float>>
using pcl::KdTreeFLANN< PointT, Dist >::ConstPtr = shared_ptr<const KdTreeFLANN<PointT, Dist> >

Definition at line 152 of file kdtree_flann.h.

FLANNIndex

template<typename PointT , typename Dist = ::flann::L2_Simple<float>>
using pcl::KdTreeFLANN< PointT, Dist >::FLANNIndex = ::flann::Index<Dist>

Definition at line 148 of file kdtree_flann.h.

IndicesConstPtr

template<typename PointT , typename Dist = ::flann::L2_Simple<float>>
using pcl::KdTreeFLANN< PointT, Dist >::IndicesConstPtr = shared_ptr<const Indices>

Definition at line 146 of file kdtree_flann.h.

IndicesPtr

template<typename PointT , typename Dist = ::flann::L2_Simple<float>>
using pcl::KdTreeFLANN< PointT, Dist >::IndicesPtr = shared_ptr<Indices>

Definition at line 145 of file kdtree_flann.h.

PointCloud

template<typename PointT , typename Dist = ::flann::L2_Simple<float>>
using pcl::KdTreeFLANN< PointT, Dist >::PointCloud = typename KdTree<PointT>::PointCloud

Definition at line 142 of file kdtree_flann.h.

PointCloudConstPtr

template<typename PointT , typename Dist = ::flann::L2_Simple<float>>
using pcl::KdTreeFLANN< PointT, Dist >::PointCloudConstPtr = typename KdTree<PointT>::PointCloudConstPtr

Definition at line 143 of file kdtree_flann.h.

Ptr

template<typename PointT , typename Dist = ::flann::L2_Simple<float>>
using pcl::KdTreeFLANN< PointT, Dist >::Ptr = shared_ptr<KdTreeFLANN<PointT, Dist> >

Definition at line 151 of file kdtree_flann.h.

Constructor & Destructor Documentation

KdTreeFLANN() [1/2]

template<typename PointT , typename Dist >
pcl::KdTreeFLANN< PointT, Dist >::KdTreeFLANN ( bool sorted = true )

Default Constructor for KdTreeFLANN.

Parameters
[in] sorted set 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 49 of file kdtree_flann.hpp.

KdTreeFLANN() [2/2]

template<typename PointT , typename Dist >
pcl::KdTreeFLANN< PointT, Dist >::KdTreeFLANN ( const KdTreeFLANN< PointT, Dist > & k )

Copy constructor.

Parameters
[in] k the tree to copy into this

Definition at line 71 of file kdtree_flann.hpp.

~KdTreeFLANN()

template<typename PointT , typename Dist = ::flann::L2_Simple<float>>
pcl::KdTreeFLANN< PointT, Dist >::~KdTreeFLANN ( )
inline

Destructor for KdTreeFLANN.

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

Definition at line 203 of file kdtree_flann.h.

Member Function Documentation

makeShared()

template<typename PointT , typename Dist = ::flann::L2_Simple<float>>
Ptr pcl::KdTreeFLANN< PointT, Dist >::makeShared ( )
inline

Definition at line 195 of file kdtree_flann.h.

nearestKSearch()

template<typename PointT , typename Dist >
int pcl::KdTreeFLANN< PointT, Dist >::nearestKSearch ( const PointT & point,
unsigned int k,
Indices & k_indices,
std::vector< float > & k_sqr_distances
) const
overridevirtual

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] point a given 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

Implements pcl::KdTree< PointT >.

Definition at line 237 of file kdtree_flann.hpp.

Referenced by pcl::gpu::DataSource::findKNNeghbors(), pcl::StatisticalMultiscaleInterestRegionExtraction< PointT >::generateCloudGraph(), pcl::getApproximateIndices(), pcl::VoxelGridCovariance< PointTarget >::nearestKSearch(), and pcl::ConcaveHull< PointInT >::performReconstruction().

operator=()

template<typename PointT , typename Dist = ::flann::L2_Simple<float>>
KdTreeFLANN<PointT, Dist>& pcl::KdTreeFLANN< PointT, Dist >::operator= ( const KdTreeFLANN< PointT, Dist > & k )
inline

Copy operator.

Parameters
[in] k the tree to copy into this

Definition at line 171 of file kdtree_flann.h.

radiusSearch()

template<typename PointT , typename Dist >
int pcl::KdTreeFLANN< PointT, Dist >::radiusSearch ( const PointT & point,
double radius,
Indices & k_indices,
std::vector< float > & k_sqr_distances,
unsigned int max_nn = 0
) const
overridevirtual

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] point a given 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

Implements pcl::KdTree< PointT >.

Definition at line 375 of file kdtree_flann.hpp.

Referenced by pcl::gpu::DataSource::findRadiusNeghbors(), pcl::VoxelGridCovariance< PointTarget >::radiusSearch(), and pcl::TextureMapping< PointInT >::textureMeshwithMultipleCameras().

setEpsilon()

template<typename PointT , typename Dist >
void pcl::KdTreeFLANN< PointT, Dist >::setEpsilon ( float eps )
overridevirtual

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

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

Reimplemented from pcl::KdTree< PointT >.

Definition at line 84 of file kdtree_flann.hpp.

setInputCloud()

template<typename PointT , typename Dist >
void pcl::KdTreeFLANN< PointT, Dist >::setInputCloud ( const PointCloudConstPtr & cloud,
const IndicesConstPtr & indices = IndicesConstPtr()
)
overridevirtual

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 from pcl::KdTree< PointT >.

Definition at line 102 of file kdtree_flann.hpp.

Referenced by pcl::VoxelGridCovariance< PointTarget >::filter(), pcl::gpu::DataSource::findKNNeghbors(), pcl::gpu::DataSource::findRadiusNeghbors(), pcl::StatisticalMultiscaleInterestRegionExtraction< PointT >::generateCloudGraph(), pcl::getApproximateIndices(), pcl::ConcaveHull< PointInT >::performReconstruction(), and pcl::TextureMapping< PointInT >::textureMeshwithMultipleCameras().

setSortedResults()

template<typename PointT , typename Dist >
void pcl::KdTreeFLANN< PointT, Dist >::setSortedResults ( bool sorted )

Definition at line 93 of file kdtree_flann.hpp.


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

© 2009–2012, Willow Garage, Inc.
© 2012–, Open Perception, Inc.
Licensed under the BSD License.
https://pointclouds.org/documentation/classpcl_1_1_kd_tree_f_l_a_n_n.html