Implementation of a simple brute force search algorithm. More...

#include <pcl/search/brute_force.h>

Inheritance diagram for pcl::search::BruteForce< PointT >:

Collaboration diagram for pcl::search::BruteForce< PointT >:

Classes
struct	Entry
Public Types
typedef PointCloud::Ptr	PointCloudPtr
typedef boost::shared_ptr < pcl::search::Search< PointT > >	Ptr
typedef boost::shared_ptr < const pcl::search::Search < PointT > >	ConstPtr
Public Member Functions
	BruteForce (bool sorted_results=false)
virtual	~BruteForce ()
	Destructor for KdTree.
int	nearestKSearch (const PointT &point, int k, std::vector< int > &k_indices, std::vector< float > &k_distances) const
	Search for the k-nearest neighbors for the given query point.
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.
virtual const std::string &	getName () const
	returns the search method name
virtual void	setSortedResults (bool sorted)
	sets whether the results should be sorted (ascending in the distance) or not
virtual void	setInputCloud (const PointCloudConstPtr &cloud, const IndicesConstPtr &indices=IndicesConstPtr())
	Pass the input dataset that the search will be performed on.
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.
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.
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).
virtual void	nearestKSearch (const PointCloud &cloud, const std::vector< int > &indices, int k, std::vector< std::vector< int > > &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	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.
template<typename PointTDiff >
void	nearestKSearchT (const pcl::PointCloud< PointTDiff > &cloud, const std::vector< int > &indices, int k, std::vector< std::vector< int > > &k_indices, std::vector< std::vector< float > > &k_sqr_distances) const
	Search for the k-nearest neighbors for the given query point.
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.
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).
virtual void	radiusSearch (const PointCloud &cloud, const std::vector< int > &indices, double radius, std::vector< std::vector< int > > &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 >
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.
template<typename PointTDiff >
void	radiusSearchT (const pcl::PointCloud< PointTDiff > &cloud, const std::vector< int > &indices, double radius, std::vector< std::vector< int > > &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.

Detailed Description

template<typename PointT>
class pcl::search::BruteForce< PointT >

Implementation of a simple brute force search algorithm.

Author:: Suat Gedikli

Definition at line 52 of file brute_force.h.

Member Typedef Documentation

template<typename PointT>

typedef boost::shared_ptr<const pcl::search::Search<PointT> > pcl::search::Search< PointT >::ConstPtr [inherited]

Reimplemented in pcl::search::FlannSearch< PointT, FlannDistance >, pcl::search::Octree< PointT, LeafTWrap, BranchTWrap, OctreeT >, pcl::search::KdTree< PointT >, and pcl::search::OrganizedNeighbor< PointT >.

Definition at line 81 of file search.h.

template<typename PointT>

typedef PointCloud::Ptr pcl::search::Search< PointT >::PointCloudPtr [inherited]

Reimplemented in pcl::search::Octree< PointT, LeafTWrap, BranchTWrap, OctreeT >, and pcl::search::OrganizedNeighbor< PointT >.

Definition at line 77 of file search.h.

template<typename PointT>

typedef boost::shared_ptr<pcl::search::Search<PointT> > pcl::search::Search< PointT >::Ptr [inherited]

Reimplemented in pcl::search::FlannSearch< PointT, FlannDistance >, pcl::search::Octree< PointT, LeafTWrap, BranchTWrap, OctreeT >, pcl::search::KdTree< PointT >, and pcl::search::OrganizedNeighbor< PointT >.

Definition at line 80 of file search.h.

Constructor & Destructor Documentation

template<typename PointT >

pcl::search::BruteForce< PointT >::BruteForce ( bool sorted_results = false ) [inline]

Definition at line 88 of file brute_force.h.

template<typename PointT >

virtual pcl::search::BruteForce< PointT >::~BruteForce ( ) [inline, virtual]

Destructor for KdTree.

Definition at line 95 of file brute_force.h.

Member Function Documentation

template<typename PointT>

virtual IndicesConstPtr pcl::search::Search< PointT >::getIndices ( ) const [inline, virtual, inherited]

Get a pointer to the vector of indices used.

Definition at line 139 of file search.h.

template<typename PointT>

virtual PointCloudConstPtr pcl::search::Search< PointT >::getInputCloud ( ) const [inline, virtual, inherited]

Get a pointer to the input point cloud dataset.

Definition at line 132 of file search.h.

template<typename PointT>

virtual const std::string& pcl::search::Search< PointT >::getName ( ) const [inline, virtual, inherited]

returns the search method name

Definition at line 104 of file search.h.

template<typename PointT >

int pcl::search::BruteForce< PointT >::nearestKSearch	(	const PointT &	point,
		int	k,
		std::vector< int > &	k_indices,
		std::vector< float > &	k_distances
	)		const `[virtual]`

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_distances	the resultant squared distances to the neighboring points (must be resized to k a priori!)

Returns:: number of neighbors found

Implements pcl::search::Search< PointT >.

Definition at line 54 of file brute_force.hpp.

template<typename PointT>

virtual int pcl::search::Search< PointT >::nearestKSearch	(	const PointCloud &	cloud,
		int	index,
		int	k,
		std::vector< int > &	k_indices,
		std::vector< float > &	k_sqr_distances
	)		const `[inline, virtual, inherited]`

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]	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

Reimplemented in pcl::search::Octree< PointT, LeafTWrap, BranchTWrap, OctreeT >.

Definition at line 195 of file search.h.

template<typename PointT>

virtual int pcl::search::Search< PointT >::nearestKSearch	(	int	index,
		int	k,
		std::vector< int > &	k_indices,
		std::vector< float > &	k_sqr_distances
	)		const `[inline, virtual, inherited]`

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.points.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

Reimplemented in pcl::search::Octree< PointT, LeafTWrap, BranchTWrap, OctreeT >.

Definition at line 220 of file search.h.

template<typename PointT>

virtual void pcl::search::Search< PointT >::nearestKSearch	(	const PointCloud &	cloud,
		const std::vector< int > &	indices,
		int	k,
		std::vector< std::vector< int > > &	k_indices,
		std::vector< std::vector< float > > &	k_sqr_distances
	)		const `[inline, virtual, inherited]`

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

Parameters:

[in]	cloud	the point cloud data
[in]	indices	a vector of point cloud indices to query for nearest neighbors
[in]	k	the number of neighbors to search for
[out]	k_indices	the resultant indices of the neighboring points, k_indices[i] corresponds to the neighbors of the query point i
[out]	k_sqr_distances	the resultant squared distances to the neighboring points, k_sqr_distances[i] corresponds to the neighbors of the query point i

Definition at line 245 of file search.h.

template<typename PointT>

template<typename PointTDiff >

int pcl::search::Search< PointT >::nearestKSearchT	(	const PointTDiff &	point,
		int	k,
		std::vector< int > &	k_indices,
		std::vector< float > &	k_sqr_distances
	)		const `[inline, inherited]`

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 166 of file search.h.

template<typename PointT>

template<typename PointTDiff >

void pcl::search::Search< PointT >::nearestKSearchT	(	const pcl::PointCloud< PointTDiff > &	cloud,
		const std::vector< int > &	indices,
		int	k,
		std::vector< std::vector< int > > &	k_indices,
		std::vector< std::vector< float > > &	k_sqr_distances
	)		const `[inline, inherited]`

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

Use this method if the query points are of a different type than the points in the data set (e.g. PointXYZRGBA instead of PointXYZ).

Parameters:

[in]	cloud	the point cloud data
[in]	indices	a vector of point cloud indices to query for nearest neighbors
[in]	k	the number of neighbors to search for
[out]	k_indices	the resultant indices of the neighboring points, k_indices[i] corresponds to the neighbors of the query point i
[out]	k_sqr_distances	the resultant squared distances to the neighboring points, k_sqr_distances[i] corresponds to the neighbors of the query point i

Note:: This method copies the input point cloud of type PointTDiff to a temporary cloud of type PointT and performs the batch search on the new cloud. You should prefer the single-point search if you don't use a search algorithm that accelerates batch NN search.

Definition at line 273 of file search.h.

template<typename PointT >

int pcl::search::BruteForce< PointT >::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.

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

Implements pcl::search::Search< PointT >.

Definition at line 335 of file brute_force.hpp.

template<typename PointT>

virtual int pcl::search::Search< PointT >::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 `[inline, virtual, inherited]`

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]	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

Reimplemented in pcl::search::Octree< PointT, LeafTWrap, BranchTWrap, OctreeT >.

Definition at line 359 of file search.h.

template<typename PointT>

virtual int pcl::search::Search< PointT >::radiusSearch	(	int	index,
		double	radius,
		std::vector< int > &	k_indices,
		std::vector< float > &	k_sqr_distances,
		unsigned int	max_nn = `0`
	)		const `[inline, virtual, inherited]`

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.points.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

Reimplemented in pcl::search::Octree< PointT, LeafTWrap, BranchTWrap, OctreeT >.

Definition at line 387 of file search.h.

template<typename PointT>

virtual void pcl::search::Search< PointT >::radiusSearch	(	const PointCloud &	cloud,
		const std::vector< int > &	indices,
		double	radius,
		std::vector< std::vector< int > > &	k_indices,
		std::vector< std::vector< float > > &	k_sqr_distances,
		unsigned int	max_nn = `0`
	)		const `[inline, virtual, inherited]`

Search for all the nearest neighbors of the query point in a given radius.

Parameters:

[in]	cloud	the point cloud data
[in]	indices	the indices in cloud. If indices is empty, neighbors will be searched for all points.
[in]	radius	the radius of the sphere bounding all of p_q's neighbors
[out]	k_indices	the resultant indices of the neighboring points, k_indices[i] corresponds to the neighbors of the query point i
[out]	k_sqr_distances	the resultant squared distances to the neighboring points, k_sqr_distances[i] corresponds to the neighbors of the query point i
[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.

Definition at line 413 of file search.h.

template<typename PointT>

template<typename PointTDiff >

int pcl::search::Search< PointT >::radiusSearchT	(	const PointTDiff &	point,
		double	radius,
		std::vector< int > &	k_indices,
		std::vector< float > &	k_sqr_distances,
		unsigned int	max_nn = `0`
	)		const `[inline, inherited]`

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

Definition at line 329 of file search.h.

template<typename PointT>

template<typename PointTDiff >

void pcl::search::Search< PointT >::radiusSearchT	(	const pcl::PointCloud< PointTDiff > &	cloud,
		const std::vector< int > &	indices,
		double	radius,
		std::vector< std::vector< int > > &	k_indices,
		std::vector< std::vector< float > > &	k_sqr_distances,
		unsigned int	max_nn = `0`
	)		const `[inline, inherited]`

Search for all the nearest neighbors of the query points in a given radius.

Parameters:

[in]	cloud	the point cloud data
[in]	indices	a vector of point cloud indices to query for nearest neighbors
[in]	radius	the radius of the sphere bounding all of p_q's neighbors
[out]	k_indices	the resultant indices of the neighboring points, k_indices[i] corresponds to the neighbors of the query point i
[out]	k_sqr_distances	the resultant squared distances to the neighboring points, k_sqr_distances[i] corresponds to the neighbors of the query point i
[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.

Note:: This method copies the input point cloud of type PointTDiff to a temporary cloud of type PointT and performs the batch search on the new cloud. You should prefer the single-point search if you don't use a search algorithm that accelerates batch NN search.

Definition at line 449 of file search.h.

template<typename PointT>

virtual void pcl::search::Search< PointT >::setInputCloud	(	const PointCloudConstPtr &	cloud,
		const IndicesConstPtr &	indices = `IndicesConstPtr ()`
	)		`[inline, virtual, inherited]`

Pass the input dataset that the search will be performed on.

Parameters:

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

Reimplemented in pcl::search::FlannSearch< PointT, FlannDistance >, and pcl::search::KdTree< PointT >.

Definition at line 124 of file search.h.

template<typename PointT>

virtual void pcl::search::Search< PointT >::setSortedResults ( bool sorted ) [inline, virtual, inherited]

sets whether the results should be sorted (ascending in the distance) or not

Parameters:

[in] sorted should be true if the results should be sorted by the distance in ascending order. Otherwise the results may be returned in any order.

Reimplemented in pcl::search::KdTree< PointT >.

Definition at line 114 of file search.h.

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

/usr/src/RPM/BUILD/PCL-1.6.0-Source/search/include/pcl/search/brute_force.h
/usr/src/RPM/BUILD/PCL-1.6.0-Source/search/include/pcl/search/impl/brute_force.hpp

Classes

Public Types

Public Member Functions

Detailed Description

template<typename PointT> class pcl::search::BruteForce< PointT >

Member Typedef Documentation

Constructor & Destructor Documentation

Member Function Documentation

template<typename PointT>
class pcl::search::BruteForce< PointT >