OrganizedNeighbor is a class for optimized nearest neigbhor search in organized point clouds. More...

#include <pcl/search/organized.h>

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

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

Classes
struct	Entry
Public Types
typedef pcl::PointCloud< PointT >	PointCloud
typedef boost::shared_ptr < PointCloud >	PointCloudPtr
typedef boost::shared_ptr < const PointCloud >	PointCloudConstPtr
typedef boost::shared_ptr < const std::vector< int > >	IndicesConstPtr
typedef boost::shared_ptr < pcl::search::OrganizedNeighbor < PointT > >	Ptr
typedef boost::shared_ptr < const pcl::search::OrganizedNeighbor < PointT > >	ConstPtr
typedef boost::shared_ptr < std::vector< int > >	IndicesPtr
Public Member Functions
	OrganizedNeighbor (bool sorted_results=false)
	OrganizedNeighbor constructor.
virtual	~OrganizedNeighbor ()
	Empty deconstructor.
bool	isValid () const
	Test whether this search-object is valid (input is organized AND from projective device) User should use this method after setting the input cloud, since setInput just prints an error if input is not organized or a projection matrix could not be determined.
void	computeCameraMatrix (Eigen::Matrix3f &camera_matrix) const
virtual void	setInputCloud (const PointCloudConstPtr &cloud, const IndicesConstPtr &indices=IndicesConstPtr())
	Provide a pointer to the input data set, if user has focal length he must set it before calling this.
int	radiusSearch (const PointT &p_q, const double radius, std::vector< int > &k_indices, std::vector< float > &k_sqr_distances, unsigned int max_nn=0) const
	Search for all neighbors of query point that are within a given radius.
void	estimateProjectionMatrix ()
	estimated the projection matrix from the input cloud
int	nearestKSearch (const PointT &p_q, int k, std::vector< int > &k_indices, std::vector< float > &k_sqr_distances) const
	Search for the k-nearest neighbors for a given query point.
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 ()
	Get a pointer to the input point cloud dataset.
virtual IndicesConstPtr const	getIndices ()
	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::OrganizedNeighbor< PointT >

OrganizedNeighbor is a class for optimized nearest neigbhor search in organized point clouds.

Author:: Radu B. Rusu, Julius Kammerl, Suat Gedikli, Koen Buys

Definition at line 61 of file organized.h.

Member Typedef Documentation

template<typename PointT >

typedef boost::shared_ptr<const pcl::search::OrganizedNeighbor<PointT> > pcl::search::OrganizedNeighbor< PointT >::ConstPtr

Reimplemented from pcl::search::Search< PointT >.

Definition at line 73 of file organized.h.

template<typename PointT >

typedef boost::shared_ptr<const std::vector<int> > pcl::search::OrganizedNeighbor< PointT >::IndicesConstPtr

Reimplemented from pcl::search::Search< PointT >.

Definition at line 70 of file organized.h.

template<typename PointT>

typedef boost::shared_ptr<std::vector<int> > pcl::search::Search< PointT >::IndicesPtr [inherited]

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

Definition at line 83 of file search.h.

template<typename PointT >

typedef pcl::PointCloud<PointT> pcl::search::OrganizedNeighbor< PointT >::PointCloud

Reimplemented from pcl::search::Search< PointT >.

Definition at line 66 of file organized.h.

template<typename PointT >

typedef boost::shared_ptr<const PointCloud> pcl::search::OrganizedNeighbor< PointT >::PointCloudConstPtr

Reimplemented from pcl::search::Search< PointT >.

Definition at line 69 of file organized.h.

template<typename PointT >

typedef boost::shared_ptr<PointCloud> pcl::search::OrganizedNeighbor< PointT >::PointCloudPtr

Reimplemented from pcl::search::Search< PointT >.

Definition at line 67 of file organized.h.

template<typename PointT >

typedef boost::shared_ptr<pcl::search::OrganizedNeighbor<PointT> > pcl::search::OrganizedNeighbor< PointT >::Ptr

Reimplemented from pcl::search::Search< PointT >.

Definition at line 72 of file organized.h.

Constructor & Destructor Documentation

template<typename PointT >

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

OrganizedNeighbor constructor.

Definition at line 80 of file organized.h.

template<typename PointT >

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

Empty deconstructor.

Definition at line 88 of file organized.h.

Member Function Documentation

template<typename PointT >

void pcl::search::OrganizedNeighbor< PointT >::computeCameraMatrix ( Eigen::Matrix3f & camera_matrix ) const

Definition at line 351 of file organized.hpp.

template<typename PointT >

void pcl::search::OrganizedNeighbor< PointT >::estimateProjectionMatrix ( )

estimated the projection matrix from the input cloud

Definition at line 378 of file organized.hpp.

template<typename PointT>

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

Get a pointer to the vector of indices used.

Definition at line 134 of file search.h.

template<typename PointT>

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

Get a pointer to the input point cloud dataset.

Definition at line 127 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 101 of file search.h.

template<typename PointT >

bool pcl::search::OrganizedNeighbor< PointT >::isValid ( ) const [inline]

Test whether this search-object is valid (input is organized AND from projective device) User should use this method after setting the input cloud, since setInput just prints an error if input is not organized or a projection matrix could not be determined.

Returns:: true if the input data is organized and from a projective device, false otherwise

Definition at line 95 of file organized.h.

template<typename PointT >

int pcl::search::OrganizedNeighbor< PointT >::nearestKSearch	(	const PointT &	p_q,
		int	k,
		std::vector< int > &	k_indices,
		std::vector< float > &	k_sqr_distances
	)		const `[virtual]`

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

Note:: limiting the maximum search radius (with setMaxDistance) can lead to a significant improvement in search speed

Parameters:

[in]	p_q	the given query point (setInputCloud must be given a-priori!)
[in]	k	the number of neighbors to search for (used only if horizontal and vertical window not given already!)
[out]	k_indices	the resultant point indices (must be resized to k beforehand!)
[out]	k_sqr_distances

Note:: this function does not return distances

Returns:: number of neighbors found

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

Definition at line 112 of file organized.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, OctreeT >.

Definition at line 190 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, OctreeT >.

Definition at line 215 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 240 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 161 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 268 of file search.h.

template<typename PointT >

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

Search for all neighbors of query point that are within 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

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

Definition at line 50 of file organized.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, OctreeT >.

Definition at line 354 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, OctreeT >.

Definition at line 382 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 408 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 324 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 444 of file search.h.

template<typename PointT >

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

Provide a pointer to the input data set, if user has focal length he must set it before calling this.

Parameters:

[in]	cloud	the const boost shared pointer to a PointCloud message
[in]	indices	the const boost shared pointer to PointIndices

Definition at line 111 of file organized.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::KdTree< PointT >.

Definition at line 119 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:

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 109 of file search.h.

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

/builddir/build/BUILD/PCL-1.5.1-Source/search/include/pcl/search/organized.h
/builddir/build/BUILD/PCL-1.5.1-Source/search/include/pcl/search/impl/organized.hpp

Classes

Public Types

Public Member Functions

Detailed Description

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

Member Typedef Documentation

Constructor & Destructor Documentation

Member Function Documentation

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