Point Cloud Library (PCL)  1.8.0-dev
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pcl::KdTreeFLANN< PointT, Dist > Class Template Reference

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

#include <pcl/kdtree/kdtree_flann.h>

+ Inheritance diagram for pcl::KdTreeFLANN< PointT, Dist >:

Public Types

typedef KdTree< PointT >
::PointCloud 
PointCloud
 
typedef KdTree< PointT >
::PointCloudConstPtr 
PointCloudConstPtr
 
typedef boost::shared_ptr
< std::vector< int > > 
IndicesPtr
 
typedef boost::shared_ptr
< const std::vector< int > > 
IndicesConstPtr
 
typedef ::flann::Index< Dist > FLANNIndex
 
typedef boost::shared_ptr
< KdTreeFLANN< PointT > > 
Ptr
 
typedef boost::shared_ptr
< const KdTreeFLANN< PointT > > 
ConstPtr
 
- Public Types inherited from pcl::KdTree< PointT >
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

 KdTreeFLANN (bool sorted=true)
 Default Constructor for KdTreeFLANN. More...
 
 KdTreeFLANN (const KdTreeFLANN< PointT > &k)
 Copy constructor. More...
 
KdTreeFLANN< PointT > & operator= (const KdTreeFLANN< PointT > &k)
 Copy operator. More...
 
void setEpsilon (float eps)
 Set the search epsilon precision (error bound) for nearest neighbors searches. More...
 
void setSortedResults (bool sorted)
 
Ptr makeShared ()
 
virtual ~KdTreeFLANN ()
 Destructor for KdTreeFLANN. More...
 
void setInputCloud (const PointCloudConstPtr &cloud, const IndicesConstPtr &indices=IndicesConstPtr())
 Provide a pointer to the input dataset. More...
 
int nearestKSearch (const PointT &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...
 
int radiusSearch (const PointT &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...
 
- 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, 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 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...
 
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 69 of file kdtree_flann.h.

Member Typedef Documentation

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

Definition at line 90 of file kdtree_flann.h.

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

Definition at line 86 of file kdtree_flann.h.

template<typename PointT, typename Dist = ::flann::L2_Simple<float>>
typedef boost::shared_ptr<const std::vector<int> > pcl::KdTreeFLANN< PointT, Dist >::IndicesConstPtr

Definition at line 84 of file kdtree_flann.h.

template<typename PointT, typename Dist = ::flann::L2_Simple<float>>
typedef boost::shared_ptr<std::vector<int> > pcl::KdTreeFLANN< PointT, Dist >::IndicesPtr

Definition at line 83 of file kdtree_flann.h.

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

Definition at line 80 of file kdtree_flann.h.

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

Definition at line 81 of file kdtree_flann.h.

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

Definition at line 89 of file kdtree_flann.h.

Constructor & Destructor Documentation

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

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 49 of file kdtree_flann.hpp.

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

Copy constructor.

Parameters
[in]kthe tree to copy into this

Definition at line 61 of file kdtree_flann.hpp.

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

Destructor for KdTreeFLANN.

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

Definition at line 136 of file kdtree_flann.h.

Member Function Documentation

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

Definition at line 131 of file kdtree_flann.h.

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

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]pointa given 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

Implements pcl::KdTree< PointT >.

Definition at line 132 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().

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

Copy operator.

Parameters
[in]kthe tree to copy into this

Definition at line 108 of file kdtree_flann.h.

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

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]pointa given 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

Implements pcl::KdTree< PointT >.

Definition at line 169 of file kdtree_flann.hpp.

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

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

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

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

Reimplemented from pcl::KdTree< PointT >.

Definition at line 74 of file kdtree_flann.hpp.

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

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 from pcl::KdTree< PointT >.

Definition at line 92 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().

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

Definition at line 83 of file kdtree_flann.hpp.


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