KdTreeFLANN is a generic type of 3D spatial locator using kD-tree structures. More...
#include <pcl/kdtree/kdtree_flann.h>
Public Types |
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| 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 |
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| KdTreeFLANN (bool sorted=true) | |
| Default Constructor for KdTreeFLANN. More... |
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| KdTreeFLANN (const KdTreeFLANN< PointT, Dist > &k) | |
| Copy constructor. More... |
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| KdTreeFLANN< PointT, Dist > & | operator= (const KdTreeFLANN< PointT, Dist > &k) |
| Copy operator. More... |
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| void | setEpsilon (float eps) override |
| Set the search epsilon precision (error bound) for nearest neighbors searches. More... |
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| void | setSortedResults (bool sorted) |
| Ptr | makeShared () |
| ~KdTreeFLANN () | |
| Destructor for KdTreeFLANN. More... |
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| void | setInputCloud (const PointCloudConstPtr &cloud, const IndicesConstPtr &indices=IndicesConstPtr()) override |
| Provide a pointer to the input dataset. More... |
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| 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... |
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| 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... |
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| Public Member Functions inherited from pcl::KdTree< PointT > |
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| KdTree (bool sorted=true) | |
| Empty constructor for KdTree. More... |
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| IndicesConstPtr | getIndices () const |
| Get a pointer to the vector of indices used. More... |
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| PointCloudConstPtr | getInputCloud () const |
| Get a pointer to the input point cloud dataset. More... |
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| void | setPointRepresentation (const PointRepresentationConstPtr &point_representation) |
| Provide a pointer to the point representation to use to convert points into k-D vectors. More... |
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| PointRepresentationConstPtr | getPointRepresentation () const |
| Get a pointer to the point representation used when converting points into k-D vectors. More... |
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| virtual | ~KdTree () |
| Destructor for KdTree. More... |
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| 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... |
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| 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... |
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| 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... |
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| 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... |
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| 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... |
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| void | setMinPts (int min_pts) |
| Minimum allowed number of k nearest neighbors points that a viable result must contain. More... |
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| int | getMinPts () const |
| Get the minimum allowed number of k nearest neighbors points that a viable result must contain. More... |
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Additional Inherited Members |
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| Protected Attributes inherited from pcl::KdTree< PointT > |
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| PointCloudConstPtr | input_ |
| The input point cloud dataset containing the points we need to use. More... |
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| IndicesConstPtr | indices_ |
| A pointer to the vector of point indices to use. More... |
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| float | epsilon_ |
| Epsilon precision (error bound) for nearest neighbors searches. More... |
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| int | min_pts_ |
| Minimum allowed number of k nearest neighbors points that a viable result must contain. More... |
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| bool | sorted_ |
| Return the radius search neighbours sorted. More... |
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| PointRepresentationConstPtr | point_representation_ |
| For converting different point structures into k-dimensional vectors for nearest-neighbor search. More... |
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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.
Definition at line 132 of file kdtree_flann.h.
Member Typedef Documentation
ConstPtr
| using pcl::KdTreeFLANN< PointT, Dist >::ConstPtr = shared_ptr<const KdTreeFLANN<PointT, Dist> > |
Definition at line 152 of file kdtree_flann.h.
FLANNIndex
| using pcl::KdTreeFLANN< PointT, Dist >::FLANNIndex = ::flann::Index<Dist> |
Definition at line 148 of file kdtree_flann.h.
IndicesConstPtr
| using pcl::KdTreeFLANN< PointT, Dist >::IndicesConstPtr = shared_ptr<const Indices> |
Definition at line 146 of file kdtree_flann.h.
IndicesPtr
| using pcl::KdTreeFLANN< PointT, Dist >::IndicesPtr = shared_ptr<Indices> |
Definition at line 145 of file kdtree_flann.h.
PointCloud
| using pcl::KdTreeFLANN< PointT, Dist >::PointCloud = typename KdTree<PointT>::PointCloud |
Definition at line 142 of file kdtree_flann.h.
PointCloudConstPtr
| using pcl::KdTreeFLANN< PointT, Dist >::PointCloudConstPtr = typename KdTree<PointT>::PointCloudConstPtr |
Definition at line 143 of file kdtree_flann.h.
Ptr
| 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]
| 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]
| 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()
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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()
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inline |
Definition at line 195 of file kdtree_flann.h.
nearestKSearch()
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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
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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=()
|
inline |
Copy operator.
- Parameters
-
[in] k the tree to copy into this
Definition at line 171 of file kdtree_flann.h.
radiusSearch()
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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()
|
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()
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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()
| 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:
- pcl/kdtree/kdtree_flann.h
- pcl/kdtree/impl/kdtree_flann.hpp
© 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
