pcl::search::KdTreeNanoflann is a faster and flexible alternative to pcl::search::KdTree. More...

#include <pcl/search/kdtree_nanoflann.h>

Inheritance diagram for pcl::search::KdTreeNanoflann< PointT, Dim, Distance, L2_Adaptor, L2_Simple_Adaptor, Tree >:

Public Types
using	PointCloud = typename Search<PointT>::PointCloud
using	PointCloudConstPtr = typename pcl::PointCloud<PointT>::ConstPtr
using	Ptr = shared_ptr<KdTreeNanoflann<PointT, Dim, Distance, Tree>>
using	ConstPtr = shared_ptr<const KdTreeNanoflann<PointT, Dim, Distance, Tree>>
using	KdTreePtr = shared_ptr<Tree>
using	KdTreeConstPtr = shared_ptr<const Tree>
using	PointRepresentationConstPtr = shared_ptr<const PointRepresentation<PointT>>
Public Types inherited from pcl::search::KdTree< PointT, Tree >
using	PointCloud = typename Search<PointT>::PointCloud
using	PointCloudConstPtr = typename Search<PointT>::PointCloudConstPtr
using	Ptr = shared_ptr<KdTree<PointT, Tree> >
using	ConstPtr = shared_ptr<const KdTree<PointT, Tree> >
using	KdTreePtr = typename Tree::Ptr
using	KdTreeConstPtr = typename Tree::ConstPtr
using	PointRepresentationConstPtr = typename PointRepresentation<PointT>::ConstPtr
Public Types inherited from pcl::search::Search< PointT >
using	PointCloud = pcl::PointCloud<PointT>
using	PointCloudPtr = typename PointCloud::Ptr
using	PointCloudConstPtr = typename PointCloud::ConstPtr
using	Ptr = shared_ptr<pcl::search::Search<PointT> >
using	ConstPtr = shared_ptr<const pcl::search::Search<PointT> >
using	IndicesPtr = pcl::IndicesPtr
using	IndicesConstPtr = pcl::IndicesConstPtr

Public Member Functions
	KdTreeNanoflann (bool sorted, std::size_t leaf_max_size, unsigned int n_thread_build=1)
	Constructor for KdTreeNanoflann.
	KdTreeNanoflann (bool sorted=false)
	Constructor for KdTreeNanoflann.
	~KdTreeNanoflann () override=default
	Destructor for KdTreeNanoflann.
void	setPointRepresentation (const PointRepresentationConstPtr &point_representation)
	Provide a pointer to the point representation to use to convert points into k-D vectors.
PointRepresentationConstPtr	getPointRepresentation () const
	Get a pointer to the point representation used when converting points into k-D vectors.
void	setSortedResults (bool sorted_results) override
	Sets whether the results have to be sorted or not.
void	setEpsilon (float eps)
	Set the search epsilon precision (error bound) for nearest neighbors searches.
float	getEpsilon () const
	Get the search epsilon precision (error bound) for nearest neighbors searches.
void	setUseRKNN (bool use_rknn)
	Influences the results of radiusSearch when max_nn is not set to zero.
bool	setInputCloud (const PointCloudConstPtr &cloud, const IndicesConstPtr &indices=IndicesConstPtr()) override
	Provide a pointer to the input dataset, this will build the kd-tree.
KdTreePtr	getNanoflannTree ()
	Get pointer to internal nanoflann tree.
int	nearestKSearch (const PointT &point, int k, Indices &k_indices, std::vector< float > &k_sqr_distances) const override
	Search for the k-nearest neighbors for the given query point.
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.
Public Member Functions inherited from pcl::search::KdTree< PointT, Tree >
	KdTree (bool sorted=true)
	Constructor for KdTree.
	~KdTree () override=default
	Destructor for KdTree.
void	setPointRepresentation (const PointRepresentationConstPtr &point_representation)
	Provide a pointer to the point representation to use to convert points into k-D vectors.
PointRepresentationConstPtr	getPointRepresentation () const
	Get a pointer to the point representation used when converting points into k-D vectors.
void	setEpsilon (float eps)
	Set the search epsilon precision (error bound) for nearest neighbors searches.
float	getEpsilon () const
	Get the search epsilon precision (error bound) for nearest neighbors searches.
Public Member Functions inherited from pcl::search::Search< PointT >
	Search (const std::string &name="", bool sorted=false)
	Constructor.
virtual	~Search ()=default
	Destructor.
virtual const std::string &	getName () const
	Returns the search method name.
virtual bool	getSortedResults ()
	Gets whether the results should be sorted (ascending in the distance) or not Otherwise the results may be returned in any order.
virtual PointCloudConstPtr	getInputCloud () const
	Get a pointer to the input point cloud dataset.
virtual IndicesConstPtr	getIndices () const
	Get a pointer to the vector of indices used.
template<typename PointTDiff>
int	nearestKSearchT (const PointTDiff &point, int k, Indices &k_indices, std::vector< float > &k_sqr_distances) const
	Search for k-nearest neighbors for the given query point.
virtual int	nearestKSearch (const PointCloud &cloud, index_t index, int k, Indices &k_indices, std::vector< float > &k_sqr_distances) const
	Search for k-nearest neighbors for the given query point.
virtual int	nearestKSearch (index_t index, int k, Indices &k_indices, std::vector< float > &k_sqr_distances) const
	Search for k-nearest neighbors for the given query point (zero-copy).
virtual void	nearestKSearch (const PointCloud &cloud, const Indices &indices, int k, std::vector< Indices > &k_indices, std::vector< std::vector< float > > &k_sqr_distances) const
	Search for the k-nearest neighbors for the given query point.
template<typename PointTDiff>
void	nearestKSearchT (const pcl::PointCloud< PointTDiff > &cloud, const Indices &indices, int k, std::vector< Indices > &k_indices, std::vector< std::vector< float > > &k_sqr_distances) const
	Search for the k-nearest neighbors for the given query point.
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.
virtual int	radiusSearch (const PointCloud &cloud, index_t 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.
virtual int	radiusSearch (index_t 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).
virtual void	radiusSearch (const PointCloud &cloud, const Indices &indices, double radius, std::vector< Indices > &k_indices, std::vector< 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.
template<typename PointTDiff>
void	radiusSearchT (const pcl::PointCloud< PointTDiff > &cloud, const Indices &indices, double radius, std::vector< Indices > &k_indices, std::vector< std::vector< float > > &k_sqr_distances, unsigned int max_nn=0) const
	Search for all the nearest neighbors of the query points in a given radius.

Additional Inherited Members
Protected Member Functions inherited from pcl::search::KdTree< PointT, Tree >
	KdTree (const std::string &name, bool sorted)
	This leaves the internal tree_ object uninitialized!
Protected Member Functions inherited from pcl::search::Search< PointT >
void	sortResults (Indices &indices, std::vector< float > &distances) const
Protected Attributes inherited from pcl::search::KdTree< PointT, Tree >
KdTreePtr	tree_
	A pointer to the internal KdTree object.
Protected Attributes inherited from pcl::search::Search< PointT >
PointCloudConstPtr	input_
IndicesConstPtr	indices_
bool	sorted_results_
std::string	name_

Detailed Description

template<typename PointT, std::int32_t Dim = 3, typename Distance = std::conditional_t<Dim >= 4, L2_Adaptor, L2_Simple_Adaptor, typename Tree = nanoflann::KDTreeSingleIndexAdaptor< Distance, pcl::search::internal::PointCloudAdaptor<float>, Dim, pcl::index_t>>
class pcl::search::KdTreeNanoflann< PointT, Dim, Distance, L2_Adaptor, L2_Simple_Adaptor, Tree >

pcl::search::KdTreeNanoflann is a faster and flexible alternative to pcl::search::KdTree.

It is based on the nanoflann library by Jose Luis Blanco-Claraco ( https://github.com/jlblancoc/nanoflann ). Since nanoflann is treated as an optional dependency to PCL, you must test the preprocessor symbol PCL_HAS_NANOFLANN after including pcl/search/kdtree_nanoflann.h.

// Example 1: using KdTreeNanoflann in NormalEstimation:
pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> ne;
auto tree = pcl::make_shared<pcl::search::KdTreeNanoflann<pcl::PointXYZ>>();
ne.setSearchMethod (tree);
// rest as usual
// Example 2: using KdTreeNanoflannn in ICP:
pcl::IterativeClosestPoint<pcl::PointXYZ, pcl::PointXYZ> icp;
icp.setSearchMethodSource(pcl::make_shared<pcl::search::KdTreeNanoflann<pcl::PointXYZ>>());
icp.setSearchMethodTarget(pcl::make_shared<pcl::search::KdTreeNanoflann<pcl::PointXYZ>>());
// rest as usual
// Example 3: using a L1 distance norm:
pcl::search::KdTreeNanoflann<PointXYZ, 3, pcl::search::L1_Adaptor> kdtree_nanoflann;
kdtree_nanoflann.setInputCloud (my_cloud);
// now ready to use kdtree_nanoflann.nearestKSearch(...) and
// kdtree_nanoflann.radiusSearch(...);
// Example 4: using KdTreeNanoflann with features instead of 3D points:
pcl::search::KdTreeNanoflann<pcl::FPFHSignature33, 33> kdtree_nanoflann_feat;
kdtree_nanoflann_feat.setInputCloud(my_features_cloud);
// now ready for searching

Template Parameters

PointT	Can be a point like pcl::PointXYZ or a feature like pcl::SHOT352
Dim	Dimension of the KdTree to build. If set to -1, it will infer the dimension at runtime from the given point representation, but with a performance penalty. Default is 3, appropriate for all xyz point types.
Distance	The distance to use. Possible values include pcl::L1_Adaptor (taxicab/manhattan distance), pcl::L2_Simple_Adaptor (euclidean distance, optimized for few dimensions), pcl::L2_Adaptor (euclidean distance, optimized for many dimensions), pcl::SO2_Adaptor, pcl::SO3_Adaptor. Default is pcl::L2_Simple_Adaptor

Author: Markus Vieth

Definition at line 186 of file kdtree_nanoflann.h.

Member Typedef Documentation

◆ ConstPtr

template<typename PointT, std::int32_t Dim = 3, typename Distance = std::conditional_t<Dim >= 4, L2_Adaptor, L2_Simple_Adaptor, typename Tree = nanoflann::KDTreeSingleIndexAdaptor< Distance, pcl::search::internal::PointCloudAdaptor<float>, Dim, pcl::index_t>>

using pcl::search::KdTreeNanoflann< PointT, Dim, Distance, L2_Adaptor, L2_Simple_Adaptor, Tree >::ConstPtr = shared_ptr<const KdTreeNanoflann<PointT, Dim, Distance, Tree>>

Definition at line 292 of file kdtree_nanoflann.h.

◆ KdTreeConstPtr

template<typename PointT, std::int32_t Dim = 3, typename Distance = std::conditional_t<Dim >= 4, L2_Adaptor, L2_Simple_Adaptor, typename Tree = nanoflann::KDTreeSingleIndexAdaptor< Distance, pcl::search::internal::PointCloudAdaptor<float>, Dim, pcl::index_t>>

using pcl::search::KdTreeNanoflann< PointT, Dim, Distance, L2_Adaptor, L2_Simple_Adaptor, Tree >::KdTreeConstPtr = shared_ptr<const Tree>

Definition at line 295 of file kdtree_nanoflann.h.

◆ KdTreePtr

template<typename PointT, std::int32_t Dim = 3, typename Distance = std::conditional_t<Dim >= 4, L2_Adaptor, L2_Simple_Adaptor, typename Tree = nanoflann::KDTreeSingleIndexAdaptor< Distance, pcl::search::internal::PointCloudAdaptor<float>, Dim, pcl::index_t>>

using pcl::search::KdTreeNanoflann< PointT, Dim, Distance, L2_Adaptor, L2_Simple_Adaptor, Tree >::KdTreePtr = shared_ptr<Tree>

Definition at line 294 of file kdtree_nanoflann.h.

◆ PointCloud

template<typename PointT, std::int32_t Dim = 3, typename Distance = std::conditional_t<Dim >= 4, L2_Adaptor, L2_Simple_Adaptor, typename Tree = nanoflann::KDTreeSingleIndexAdaptor< Distance, pcl::search::internal::PointCloudAdaptor<float>, Dim, pcl::index_t>>

using pcl::search::KdTreeNanoflann< PointT, Dim, Distance, L2_Adaptor, L2_Simple_Adaptor, Tree >::PointCloud = typename Search<PointT>::PointCloud

Definition at line 279 of file kdtree_nanoflann.h.

◆ PointCloudConstPtr

template<typename PointT, std::int32_t Dim = 3, typename Distance = std::conditional_t<Dim >= 4, L2_Adaptor, L2_Simple_Adaptor, typename Tree = nanoflann::KDTreeSingleIndexAdaptor< Distance, pcl::search::internal::PointCloudAdaptor<float>, Dim, pcl::index_t>>

using pcl::search::KdTreeNanoflann< PointT, Dim, Distance, L2_Adaptor, L2_Simple_Adaptor, Tree >::PointCloudConstPtr = typename pcl::PointCloud<PointT>::ConstPtr

Definition at line 280 of file kdtree_nanoflann.h.

◆ PointRepresentationConstPtr

template<typename PointT, std::int32_t Dim = 3, typename Distance = std::conditional_t<Dim >= 4, L2_Adaptor, L2_Simple_Adaptor, typename Tree = nanoflann::KDTreeSingleIndexAdaptor< Distance, pcl::search::internal::PointCloudAdaptor<float>, Dim, pcl::index_t>>

using pcl::search::KdTreeNanoflann< PointT, Dim, Distance, L2_Adaptor, L2_Simple_Adaptor, Tree >::PointRepresentationConstPtr = shared_ptr<const PointRepresentation<PointT>>

Definition at line 296 of file kdtree_nanoflann.h.

◆ Ptr

template<typename PointT, std::int32_t Dim = 3, typename Distance = std::conditional_t<Dim >= 4, L2_Adaptor, L2_Simple_Adaptor, typename Tree = nanoflann::KDTreeSingleIndexAdaptor< Distance, pcl::search::internal::PointCloudAdaptor<float>, Dim, pcl::index_t>>

using pcl::search::KdTreeNanoflann< PointT, Dim, Distance, L2_Adaptor, L2_Simple_Adaptor, Tree >::Ptr = shared_ptr<KdTreeNanoflann<PointT, Dim, Distance, Tree>>

Definition at line 291 of file kdtree_nanoflann.h.

Constructor & Destructor Documentation

◆ KdTreeNanoflann() [1/2]

template<typename PointT, std::int32_t Dim = 3, typename Distance = std::conditional_t<Dim >= 4, L2_Adaptor, L2_Simple_Adaptor, typename Tree = nanoflann::KDTreeSingleIndexAdaptor< Distance, pcl::search::internal::PointCloudAdaptor<float>, Dim, pcl::index_t>>

pcl::search::KdTreeNanoflann< PointT, Dim, Distance, L2_Adaptor, L2_Simple_Adaptor, Tree >::KdTreeNanoflann	(	bool	sorted,
		std::size_t	leaf_max_size,
		unsigned int	n_thread_build = 1 )

inline

Constructor for KdTreeNanoflann.

Parameters

[in]	sorted	Set to true if the nearest neighbor search results need to be sorted in ascending order based on their distance to the query point (default is false, which is faster)
[in]	leaf_max_size	Max number of points/samples in a leaf, can be tuned to achieve best performance. When using this for 1nn search (e.g. correspondence estimation in icp/registration), then leaf_max_size=10 is usually fastest. Otherwise, leaf_max_size=20 is a good choice.
[in]	n_thread_build	Number of threads for concurrent tree build (default is 1, meaning no concurrent build)

Definition at line 310 of file kdtree_nanoflann.h.

References pcl::search::KdTree< PointT, Tree >::KdTree().

Referenced by KdTreeNanoflann().

◆ KdTreeNanoflann() [2/2]

template<typename PointT, std::int32_t Dim = 3, typename Distance = std::conditional_t<Dim >= 4, L2_Adaptor, L2_Simple_Adaptor, typename Tree = nanoflann::KDTreeSingleIndexAdaptor< Distance, pcl::search::internal::PointCloudAdaptor<float>, Dim, pcl::index_t>>

pcl::search::KdTreeNanoflann< PointT, Dim, Distance, L2_Adaptor, L2_Simple_Adaptor, Tree >::KdTreeNanoflann ( bool sorted = false )

inline

Constructor for KdTreeNanoflann.

Parameters

[in] sorted set to true if the nearest neighbor search results need to be sorted in ascending order based on their distance to the query point

Definition at line 325 of file kdtree_nanoflann.h.

References KdTreeNanoflann().

◆ ~KdTreeNanoflann()

template<typename PointT, std::int32_t Dim = 3, typename Distance = std::conditional_t<Dim >= 4, L2_Adaptor, L2_Simple_Adaptor, typename Tree = nanoflann::KDTreeSingleIndexAdaptor< Distance, pcl::search::internal::PointCloudAdaptor<float>, Dim, pcl::index_t>>

pcl::search::KdTreeNanoflann< PointT, Dim, Distance, L2_Adaptor, L2_Simple_Adaptor, Tree >::~KdTreeNanoflann ( )

overridedefault

Destructor for KdTreeNanoflann.

Member Function Documentation

◆ getEpsilon()

template<typename PointT, std::int32_t Dim = 3, typename Distance = std::conditional_t<Dim >= 4, L2_Adaptor, L2_Simple_Adaptor, typename Tree = nanoflann::KDTreeSingleIndexAdaptor< Distance, pcl::search::internal::PointCloudAdaptor<float>, Dim, pcl::index_t>>

float pcl::search::KdTreeNanoflann< PointT, Dim, Distance, L2_Adaptor, L2_Simple_Adaptor, Tree >::getEpsilon ( ) const

inline

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

Definition at line 386 of file kdtree_nanoflann.h.

◆ getNanoflannTree()

template<typename PointT, std::int32_t Dim = 3, typename Distance = std::conditional_t<Dim >= 4, L2_Adaptor, L2_Simple_Adaptor, typename Tree = nanoflann::KDTreeSingleIndexAdaptor< Distance, pcl::search::internal::PointCloudAdaptor<float>, Dim, pcl::index_t>>

KdTreePtr pcl::search::KdTreeNanoflann< PointT, Dim, Distance, L2_Adaptor, L2_Simple_Adaptor, Tree >::getNanoflannTree ( )

inline

Get pointer to internal nanoflann tree.

Use with caution.

Definition at line 420 of file kdtree_nanoflann.h.

◆ getPointRepresentation()

template<typename PointT, std::int32_t Dim = 3, typename Distance = std::conditional_t<Dim >= 4, L2_Adaptor, L2_Simple_Adaptor, typename Tree = nanoflann::KDTreeSingleIndexAdaptor< Distance, pcl::search::internal::PointCloudAdaptor<float>, Dim, pcl::index_t>>

PointRepresentationConstPtr pcl::search::KdTreeNanoflann< PointT, Dim, Distance, L2_Adaptor, L2_Simple_Adaptor, Tree >::getPointRepresentation ( ) const

inline

Get a pointer to the point representation used when converting points into k-D vectors.

Definition at line 359 of file kdtree_nanoflann.h.

◆ nearestKSearch()

template<typename PointT, std::int32_t Dim = 3, typename Distance = std::conditional_t<Dim >= 4, L2_Adaptor, L2_Simple_Adaptor, typename Tree = nanoflann::KDTreeSingleIndexAdaptor< Distance, pcl::search::internal::PointCloudAdaptor<float>, Dim, pcl::index_t>>

int pcl::search::KdTreeNanoflann< PointT, Dim, Distance, L2_Adaptor, L2_Simple_Adaptor, Tree >::nearestKSearch	(	const PointT &	point,
		int	k,
		Indices &	k_indices,
		std::vector< float > &	k_sqr_distances ) const

inlineoverridevirtual

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

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

Reimplemented from pcl::search::KdTree< PointT, Tree >.

Definition at line 435 of file kdtree_nanoflann.h.

◆ radiusSearch()

template<typename PointT, std::int32_t Dim = 3, typename Distance = std::conditional_t<Dim >= 4, L2_Adaptor, L2_Simple_Adaptor, typename Tree = nanoflann::KDTreeSingleIndexAdaptor< Distance, pcl::search::internal::PointCloudAdaptor<float>, Dim, pcl::index_t>>

int pcl::search::KdTreeNanoflann< PointT, Dim, Distance, L2_Adaptor, L2_Simple_Adaptor, Tree >::radiusSearch	(	const PointT &	point,
		double	radius,
		Indices &	k_indices,
		std::vector< float > &	k_sqr_distances,
		unsigned int	max_nn = 0 ) const

inlineoverridevirtual

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

Reimplemented from pcl::search::KdTree< PointT, Tree >.

Definition at line 486 of file kdtree_nanoflann.h.

References pcl::search::Search< PointT >::sorted_results_, pcl::search::Search< PointT >::sortResults(), and pcl::search::internal::square_if_l2().

◆ setEpsilon()

template<typename PointT, std::int32_t Dim = 3, typename Distance = std::conditional_t<Dim >= 4, L2_Adaptor, L2_Simple_Adaptor, typename Tree = nanoflann::KDTreeSingleIndexAdaptor< Distance, pcl::search::internal::PointCloudAdaptor<float>, Dim, pcl::index_t>>

void pcl::search::KdTreeNanoflann< PointT, Dim, Distance, L2_Adaptor, L2_Simple_Adaptor, Tree >::setEpsilon ( float eps )

inline

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

Parameters

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

Definition at line 378 of file kdtree_nanoflann.h.

◆ setInputCloud()

template<typename PointT, std::int32_t Dim = 3, typename Distance = std::conditional_t<Dim >= 4, L2_Adaptor, L2_Simple_Adaptor, typename Tree = nanoflann::KDTreeSingleIndexAdaptor< Distance, pcl::search::internal::PointCloudAdaptor<float>, Dim, pcl::index_t>>

bool pcl::search::KdTreeNanoflann< PointT, Dim, Distance, L2_Adaptor, L2_Simple_Adaptor, Tree >::setInputCloud	(	const PointCloudConstPtr &	cloud,
		const IndicesConstPtr &	indices = IndicesConstPtr() )

inlineoverridevirtual

Provide a pointer to the input dataset, this will build the kd-tree.

Parameters

[in]	cloud	the const shared pointer to a PointCloud
[in]	indices	the point indices subset that is to be used from cloud

Reimplemented from pcl::search::KdTree< PointT, Tree >.

Definition at line 408 of file kdtree_nanoflann.h.

References pcl::search::Search< PointT >::indices_, and pcl::search::Search< PointT >::input_.

◆ setPointRepresentation()

template<typename PointT, std::int32_t Dim = 3, typename Distance = std::conditional_t<Dim >= 4, L2_Adaptor, L2_Simple_Adaptor, typename Tree = nanoflann::KDTreeSingleIndexAdaptor< Distance, pcl::search::internal::PointCloudAdaptor<float>, Dim, pcl::index_t>>

void pcl::search::KdTreeNanoflann< PointT, Dim, Distance, L2_Adaptor, L2_Simple_Adaptor, Tree >::setPointRepresentation ( const PointRepresentationConstPtr & point_representation )

inline

Provide a pointer to the point representation to use to convert points into k-D vectors.

If you want to use this function, it is recommended to do so before calling setInputCloud, to avoid setting up the kd-tree twice.

Parameters

[in] point_representation the const shared pointer to a PointRepresentation

Definition at line 336 of file kdtree_nanoflann.h.

◆ setSortedResults()

template<typename PointT, std::int32_t Dim = 3, typename Distance = std::conditional_t<Dim >= 4, L2_Adaptor, L2_Simple_Adaptor, typename Tree = nanoflann::KDTreeSingleIndexAdaptor< Distance, pcl::search::internal::PointCloudAdaptor<float>, Dim, pcl::index_t>>

void pcl::search::KdTreeNanoflann< PointT, Dim, Distance, L2_Adaptor, L2_Simple_Adaptor, Tree >::setSortedResults ( bool sorted_results )

inlineoverridevirtual

Sets whether the results have to be sorted or not.

Parameters

[in] sorted_results set to true if the radius search results should be sorted

Reimplemented from pcl::search::KdTree< PointT, Tree >.

Definition at line 368 of file kdtree_nanoflann.h.

References pcl::search::Search< PointT >::sorted_results_.

◆ setUseRKNN()

template<typename PointT, std::int32_t Dim = 3, typename Distance = std::conditional_t<Dim >= 4, L2_Adaptor, L2_Simple_Adaptor, typename Tree = nanoflann::KDTreeSingleIndexAdaptor< Distance, pcl::search::internal::PointCloudAdaptor<float>, Dim, pcl::index_t>>

void pcl::search::KdTreeNanoflann< PointT, Dim, Distance, L2_Adaptor, L2_Simple_Adaptor, Tree >::setUseRKNN ( bool use_rknn )

inline

Influences the results of radiusSearch when max_nn is not set to zero.

If the parameter max_nn of radiusSearch is set to a value greater than zero, it will return at most that many points. If you set use_rknn=true, it will always return the points closest to the query point. If you set use_rknn=false, it may return any points within the radius, which can be faster.

Definition at line 398 of file kdtree_nanoflann.h.

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

pcl/search/kdtree_nanoflann.h

Public Types

Public Member Functions

Additional Inherited Members

Detailed Description

Member Typedef Documentation

◆ ConstPtr

◆ KdTreeConstPtr

◆ KdTreePtr

◆ PointCloud

◆ PointCloudConstPtr

◆ PointRepresentationConstPtr

◆ Ptr

Constructor & Destructor Documentation

◆ KdTreeNanoflann() [1/2]

◆ KdTreeNanoflann() [2/2]

◆ ~KdTreeNanoflann()

Member Function Documentation

◆ getEpsilon()

◆ getNanoflannTree()

◆ getPointRepresentation()

◆ nearestKSearch()

◆ radiusSearch()

◆ setEpsilon()

◆ setInputCloud()

◆ setPointRepresentation()

◆ setSortedResults()

◆ setUseRKNN()