SamplingSurfaceNormal divides the input space into grids until each grid contains a maximum of N points, and samples points randomly within each grid. Normal is computed using the N points of each grid. All points sampled within a grid are assigned the same normal. More...

#include <sampling_surface_normal.h>

Inheritance diagram for pcl::SamplingSurfaceNormal< PointT >:

[legend]

List of all members.

Classes
struct	CompareDim
	CompareDim is a comparator object for sorting across a specific dimenstion (i,.e X, Y or Z) More...
Public Types
typedef boost::shared_ptr < const SamplingSurfaceNormal < PointT > >	ConstPtr
typedef boost::shared_ptr < SamplingSurfaceNormal < PointT > >	Ptr
Public Member Functions
float	getRatio () const
	Get the value of the internal ratio parameter.
unsigned int	getSample () const
	Get the value of the internal sample parameter.
unsigned int	getSeed () const
	Get the value of the internal seed parameter.
	SamplingSurfaceNormal ()
	Empty constructor.
void	setRatio (float ratio)
	Set ratio of points to be sampled in each grid.
void	setSample (unsigned int sample)
	Set maximum number of samples in each grid.
void	setSeed (unsigned int seed)
	Set seed of random function.
Protected Member Functions
void	applyFilter (PointCloud &output)
	Sample of point indices into a separate PointCloud.
Protected Attributes
float	ratio_
	Ratio of points to be sampled in each grid.
unsigned int	sample_
	Maximum number of samples in each grid.
unsigned int	seed_
	Random number seed.
Private Types
typedef Filter< PointT > ::PointCloud	PointCloud
typedef PointCloud::ConstPtr	PointCloudConstPtr
typedef PointCloud::Ptr	PointCloudPtr
typedef Eigen::Matrix< float, Eigen::Dynamic, 1 >	Vector
Private Member Functions
unsigned int	computeMeanAndCovarianceMatrix (const pcl::PointCloud< PointT > &cloud, Eigen::Matrix3f &covariance_matrix, Eigen::Vector4f &centroid)
	Computes the covariance matrix for points in the cloud. This is a port from features to avoid features dependency for filters.
void	computeNormal (const PointCloud &cloud, Eigen::Vector4f &normal, float &curvature)
	Computes the normal for points in a grid. This is a port from features to avoid features dependency for filters.
float	findCutVal (const PointCloud &cloud, const int cut_dim, const int cut_index)
	Returns the threshold for splitting in a given dimension.
void	findXYZMaxMin (const PointCloud &cloud, Vector &max_vec, Vector &min_vec)
	Finds the max and min values in each dimension.
void	partition (const PointCloud &cloud, const int first, const int last, const Vector min_values, const Vector max_values, std::vector< int > &indices, PointCloud &outcloud)
	Recursively partition the point cloud, stopping when each grid contains less than sample_ points Points are randomly sampled when a grid is found.
void	samplePartition (const PointCloud &data, const int first, const int last, std::vector< int > &indices, PointCloud &outcloud)
	Randomly sample the points in each grid.
void	solvePlaneParameters (const Eigen::Matrix3f &covariance_matrix, float &nx, float &ny, float &nz, float &curvature)
	Solve the eigenvalues and eigenvectors of a given 3x3 covariance matrix, and estimate the least-squares plane normal and surface curvature.

Detailed Description

template<typename PointT>
class pcl::SamplingSurfaceNormal< PointT >

SamplingSurfaceNormal divides the input space into grids until each grid contains a maximum of N points, and samples points randomly within each grid. Normal is computed using the N points of each grid. All points sampled within a grid are assigned the same normal.

Author:: Aravindhan K Krishnan. This code is ported from libpointmatcher (https://github.com/ethz-asl/libpointmatcher)

Definition at line 55 of file sampling_surface_normal.h.

Member Typedef Documentation

template<typename PointT >

typedef boost::shared_ptr< const SamplingSurfaceNormal<PointT> > pcl::SamplingSurfaceNormal< PointT >::ConstPtr

Reimplemented from pcl::Filter< PointT >.

Definition at line 71 of file sampling_surface_normal.h.

template<typename PointT >

typedef Filter<PointT>::PointCloud pcl::SamplingSurfaceNormal< PointT >::PointCloud [private]

Reimplemented from pcl::Filter< PointT >.

Definition at line 62 of file sampling_surface_normal.h.

template<typename PointT >

typedef PointCloud::ConstPtr pcl::SamplingSurfaceNormal< PointT >::PointCloudConstPtr [private]

Reimplemented from pcl::Filter< PointT >.

Definition at line 64 of file sampling_surface_normal.h.

template<typename PointT >

typedef PointCloud::Ptr pcl::SamplingSurfaceNormal< PointT >::PointCloudPtr [private]

Reimplemented from pcl::Filter< PointT >.

Definition at line 63 of file sampling_surface_normal.h.

template<typename PointT >

typedef boost::shared_ptr< SamplingSurfaceNormal<PointT> > pcl::SamplingSurfaceNormal< PointT >::Ptr

Reimplemented from pcl::Filter< PointT >.

Definition at line 70 of file sampling_surface_normal.h.

template<typename PointT >

typedef Eigen::Matrix<float, Eigen::Dynamic, 1> pcl::SamplingSurfaceNormal< PointT >::Vector [private]

Definition at line 66 of file sampling_surface_normal.h.

Constructor & Destructor Documentation

template<typename PointT >

pcl::SamplingSurfaceNormal< PointT >::SamplingSurfaceNormal ( ) [inline]

Empty constructor.

Definition at line 74 of file sampling_surface_normal.h.

Member Function Documentation

template<typename PointT >

void pcl::SamplingSurfaceNormal< PointT >::applyFilter ( PointCloud & output ) [protected, virtual]

Sample of point indices into a separate PointCloud.

Parameters:

[out] output the resultant point cloud

Implements pcl::Filter< PointT >.

Definition at line 48 of file sampling_surface_normal.hpp.

template<typename PointT >

unsigned int pcl::SamplingSurfaceNormal< PointT >::computeMeanAndCovarianceMatrix	(	const pcl::PointCloud< PointT > &	cloud,
		Eigen::Matrix3f &	covariance_matrix,
		Eigen::Vector4f &	centroid
	)		`[inline, private]`

Computes the covariance matrix for points in the cloud. This is a port from features to avoid features dependency for filters.

Parameters:

[in]	cloud	The input cloud
[out]	covariance_matrix	the covariance matrix
[out]	centroid	the centroid

Definition at line 231 of file sampling_surface_normal.hpp.

template<typename PointT >

void pcl::SamplingSurfaceNormal< PointT >::computeNormal	(	const PointCloud &	cloud,
		Eigen::Vector4f &	normal,
		float &	curvature
	)		`[private]`

Computes the normal for points in a grid. This is a port from features to avoid features dependency for filters.

Parameters:

[in]	cloud	The input cloud
[out]	normal	the computed normal
[out]	curvature	the computed curvature

Definition at line 204 of file sampling_surface_normal.hpp.

template<typename PointT >

float pcl::SamplingSurfaceNormal< PointT >::findCutVal	(	const PointCloud &	cloud,
		const int	cut_dim,
		const int	cut_index
	)		`[private]`

Returns the threshold for splitting in a given dimension.

Parameters:

[in]	cloud	the input cloud
[in]	cut_dim	the input dimension (0=x, 1=y, 2=z)
[in]	cut_index	the input index in the cloud

Definition at line 297 of file sampling_surface_normal.hpp.

template<typename PointT >

void pcl::SamplingSurfaceNormal< PointT >::findXYZMaxMin	(	const PointCloud &	cloud,
		Vector &	max_vec,
		Vector &	min_vec
	)		`[private]`

Finds the max and min values in each dimension.

Parameters:

[in]	cloud	the input cloud
[out]	max_vec	the max value vector
[out]	min_vec	the min value vector

Definition at line 66 of file sampling_surface_normal.hpp.

template<typename PointT >

float pcl::SamplingSurfaceNormal< PointT >::getRatio ( ) const [inline]

Get the value of the internal ratio parameter.

Definition at line 125 of file sampling_surface_normal.h.

template<typename PointT >

unsigned int pcl::SamplingSurfaceNormal< PointT >::getSample ( ) const [inline]

Get the value of the internal sample parameter.

Definition at line 92 of file sampling_surface_normal.h.

template<typename PointT >

unsigned int pcl::SamplingSurfaceNormal< PointT >::getSeed ( ) const [inline]

Get the value of the internal seed parameter.

Definition at line 109 of file sampling_surface_normal.h.

template<typename PointT >

void pcl::SamplingSurfaceNormal< PointT >::partition	(	const PointCloud &	cloud,
		const int	first,
		const int	last,
		const Vector	min_values,
		const Vector	max_values,
		std::vector< int > &	indices,
		PointCloud &	outcloud
	)		`[private]`

Recursively partition the point cloud, stopping when each grid contains less than sample_ points Points are randomly sampled when a grid is found.

Parameters:

	cloud
	first
	last
	min_values
	max_values
	indices
[out]	outcloud	output the resultant point cloud

Definition at line 122 of file sampling_surface_normal.hpp.

template<typename PointT >

void pcl::SamplingSurfaceNormal< PointT >::samplePartition	(	const PointCloud &	data,
		const int	first,
		const int	last,
		std::vector< int > &	indices,
		PointCloud &	outcloud
	)		`[private]`

Randomly sample the points in each grid.

Parameters:

[in]	data
[in]	first
[in]	last
[out]	indices
[out]	output	the resultant point cloud

Definition at line 161 of file sampling_surface_normal.hpp.

template<typename PointT >

void pcl::SamplingSurfaceNormal< PointT >::setRatio ( float ratio ) [inline]

Set ratio of points to be sampled in each grid.

Parameters:

[in] ratio sample the ratio of points to be sampled in each grid

Definition at line 118 of file sampling_surface_normal.h.

template<typename PointT >

void pcl::SamplingSurfaceNormal< PointT >::setSample ( unsigned int sample ) [inline]

Set maximum number of samples in each grid.

Parameters:

[in] sample maximum number of samples in each grid

Definition at line 85 of file sampling_surface_normal.h.

template<typename PointT >

void pcl::SamplingSurfaceNormal< PointT >::setSeed ( unsigned int seed ) [inline]

Set seed of random function.

Parameters:

[in] seed the input seed

Definition at line 101 of file sampling_surface_normal.h.

template<typename PointT >

void pcl::SamplingSurfaceNormal< PointT >::solvePlaneParameters	(	const Eigen::Matrix3f &	covariance_matrix,
		float &	nx,
		float &	ny,
		float &	nz,
		float &	curvature
	)		`[private]`

Solve the eigenvalues and eigenvectors of a given 3x3 covariance matrix, and estimate the least-squares plane normal and surface curvature.

Parameters:

[in]	covariance_matrix	the 3x3 covariance matrix
[out]	(nx	ny nz) plane_parameters the resultant plane parameters as: a, b, c, d (ax + by + cz + d = 0)
[out]	curvature	the estimated surface curvature as a measure of

Definition at line 275 of file sampling_surface_normal.hpp.

Member Data Documentation

template<typename PointT >

float pcl::SamplingSurfaceNormal< PointT >::ratio_ [protected]

Ratio of points to be sampled in each grid.

Definition at line 137 of file sampling_surface_normal.h.

template<typename PointT >

unsigned int pcl::SamplingSurfaceNormal< PointT >::sample_ [protected]

Maximum number of samples in each grid.

Definition at line 133 of file sampling_surface_normal.h.

template<typename PointT >

unsigned int pcl::SamplingSurfaceNormal< PointT >::seed_ [protected]

Random number seed.

Definition at line 135 of file sampling_surface_normal.h.

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

Classes

Public Types

Public Member Functions

Protected Member Functions

Protected Attributes

Private Types

Private Member Functions

Detailed Description

template<typename PointT> class pcl::SamplingSurfaceNormal< PointT >

Member Typedef Documentation

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation

template<typename PointT>
class pcl::SamplingSurfaceNormal< PointT >