KdTree represents the base spatial locator class for kd-tree implementations. More...
#include <pcl/kdtree/kdtree.h>
Inheritance diagram for pcl::KdTree< PointT >:
Public Types | |
| using | IndicesPtr = shared_ptr< std::vector< int > > |
| using | IndicesConstPtr = shared_ptr< const std::vector< int > > |
| 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, std::vector< int > &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, 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, unsigned 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, unsigned 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 PointT &p_q, double radius, std::vector< int > &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, 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... | |
| 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.
Member Typedef Documentation
◆ ConstPtr
| using pcl::KdTree< PointT >::ConstPtr = shared_ptr<const KdTree<PointT> > |
◆ IndicesConstPtr
| using pcl::KdTree< PointT >::IndicesConstPtr = shared_ptr<const std::vector<int> > |
◆ IndicesPtr
| using pcl::KdTree< PointT >::IndicesPtr = shared_ptr<std::vector<int> > |
◆ PointCloud
| using pcl::KdTree< PointT >::PointCloud = pcl::PointCloud<PointT> |
◆ PointCloudConstPtr
| using pcl::KdTree< PointT >::PointCloudConstPtr = typename PointCloud::ConstPtr |
◆ PointCloudPtr
| using pcl::KdTree< PointT >::PointCloudPtr = typename PointCloud::Ptr |
◆ PointRepresentation
| using pcl::KdTree< PointT >::PointRepresentation = pcl::PointRepresentation<PointT> |
◆ PointRepresentationConstPtr
| using pcl::KdTree< PointT >::PointRepresentationConstPtr = typename PointRepresentation::ConstPtr |
◆ Ptr
| using pcl::KdTree< PointT >::Ptr = shared_ptr<KdTree<PointT> > |
Constructor & Destructor Documentation
◆ KdTree()
|
inline |
◆ ~KdTree()
|
inlinevirtual |
Member Function Documentation
◆ getEpsilon()
|
inline |
◆ getIndices()
|
inline |
Get a pointer to the vector of indices used.
Definition at line 93 of file kdtree.h.
Referenced by pcl::extractEuclideanClusters().
◆ getInputCloud()
|
inline |
Get a pointer to the input point cloud dataset.
Definition at line 100 of file kdtree.h.
Referenced by pcl::extractEuclideanClusters().
◆ getMinPts()
|
inline |
◆ getName()
|
protectedpure virtual |
Class getName method.
◆ getPointRepresentation()
|
inline |
◆ nearestKSearch() [1/3]
|
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
◆ nearestKSearch() [2/3]
|
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::InterestPoint >, and pcl::KdTreeFLANN< FeatureT >.
Referenced by pcl::KdTree< FeatureT >::nearestKSearch(), and pcl::KdTree< FeatureT >::nearestKSearchT().
◆ nearestKSearch() [3/3]
|
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
◆ nearestKSearchT()
|
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]
|
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
◆ radiusSearch() [2/3]
|
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::InterestPoint >, and pcl::KdTreeFLANN< FeatureT >.
Referenced by pcl::extractEuclideanClusters(), pcl::KdTree< FeatureT >::radiusSearch(), and pcl::KdTree< FeatureT >::radiusSearchT().
◆ radiusSearch() [3/3]
|
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
◆ radiusSearchT()
|
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
◆ setEpsilon()
|
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::InterestPoint >, and pcl::KdTreeFLANN< FeatureT >.
◆ setInputCloud()
|
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::InterestPoint >, and pcl::KdTreeFLANN< FeatureT >.
Definition at line 85 of file kdtree.h.
Referenced by pcl::KdTree< FeatureT >::setPointRepresentation().
◆ setMinPts()
|
inline |
◆ setPointRepresentation()
|
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
Member Data Documentation
◆ 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_
|
protected |
A pointer to the vector of point indices to use.
Definition at line 339 of file kdtree.h.
Referenced by pcl::KdTree< FeatureT >::getIndices(), pcl::KdTree< FeatureT >::nearestKSearch(), pcl::KdTree< FeatureT >::radiusSearch(), pcl::KdTree< FeatureT >::setInputCloud(), and pcl::KdTree< FeatureT >::setPointRepresentation().
◆ input_
|
protected |
The input point cloud dataset containing the points we need to use.
Definition at line 336 of file kdtree.h.
Referenced by pcl::KdTree< FeatureT >::getInputCloud(), pcl::KdTree< FeatureT >::nearestKSearch(), pcl::KdTree< FeatureT >::radiusSearch(), pcl::KdTree< FeatureT >::setInputCloud(), and pcl::KdTree< FeatureT >::setPointRepresentation().
◆ 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_
|
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_
|
protected |
The documentation for this class was generated from the following file:
- pcl/kdtree/kdtree.h