SampleConsensusInitialAlignment is an implementation of the initial alignment algorithm described in section IV of "Fast Point Feature Histograms (FPFH) for 3D Registration," Rusu et al. More...

#include <ia_ransac.h>

Inheritance diagram for pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >:

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List of all members.

Classes
class	ErrorFunctor
class	HuberPenalty
class	TruncatedError
Public Types
typedef pcl::PointCloud< FeatureT >	FeatureCloud
typedef FeatureCloud::ConstPtr	FeatureCloudConstPtr
typedef FeatureCloud::Ptr	FeatureCloudPtr
typedef KdTreeFLANN< FeatureT > ::Ptr	FeatureKdTreePtr
typedef Registration < PointSource, PointTarget > ::PointCloudSource	PointCloudSource
typedef PointCloudSource::ConstPtr	PointCloudSourceConstPtr
typedef PointCloudSource::Ptr	PointCloudSourcePtr
typedef Registration < PointSource, PointTarget > ::PointCloudTarget	PointCloudTarget
typedef PointIndices::ConstPtr	PointIndicesConstPtr
typedef PointIndices::Ptr	PointIndicesPtr
Public Member Functions
int	getCorrespondenceRandomness ()
	Get the number of neighbors used when selecting a random feature correspondence, as set by the user.
boost::shared_ptr< ErrorFunctor >	getErrorFunction ()
	Get a shared pointer to the ErrorFunctor that is to be minimized.
float	getMinSampleDistance ()
	Get the minimum distances between samples, as set by the user.
int	getNumberOfSamples ()
	Get the number of samples to use during each iteration, as set by the user.
FeatureCloudConstPtr const	getSourceFeatures ()
	Get a pointer to the source point cloud's features.
FeatureCloudConstPtr const	getTargetFeatures ()
	Get a pointer to the target point cloud's features.
	SampleConsensusInitialAlignment ()
	Constructor.
void	setCorrespondenceRandomness (int k)
	Set the number of neighbors to use when selecting a random feature correspondence. A higher value will add more randomness to the feature matching.
void	setErrorFunction (const boost::shared_ptr< ErrorFunctor > &error_functor)
	Specify the error function to minimize.
void	setMinSampleDistance (float min_sample_distance)
	Set the minimum distances between samples.
void	setNumberOfSamples (int nr_samples)
	Set the number of samples to use during each iteration.
void	setSourceFeatures (const FeatureCloudConstPtr &features)
	Provide a boost shared pointer to the source point cloud's feature descriptors.
void	setTargetFeatures (const FeatureCloudConstPtr &features)
	Provide a boost shared pointer to the target point cloud's feature descriptors.
Protected Member Functions
float	computeErrorMetric (const PointCloudSource &cloud, float threshold)
	An error metric for that computes the quality of the alignment between the given cloud and the target.
virtual void	computeTransformation (PointCloudSource &output, const Eigen::Matrix4f &guess)
	Rigid transformation computation method.
void	findSimilarFeatures (const FeatureCloud &input_features, const std::vector< int > &sample_indices, std::vector< int > &corresponding_indices)
	For each of the sample points, find a list of points in the target cloud whose features are similar to the sample points' features. From these, select one randomly which will be considered that sample point's correspondence.
int	getRandomIndex (int n)
	Choose a random index between 0 and n-1.
void	selectSamples (const PointCloudSource &cloud, int nr_samples, float min_sample_distance, std::vector< int > &sample_indices)
	Select nr_samples sample points from cloud while making sure that their pairwise distances are greater than a user-defined minimum distance, min_sample_distance.
Protected Attributes
boost::shared_ptr< ErrorFunctor >	error_functor_
FeatureKdTreePtr	feature_tree_
	The KdTree used to compare feature descriptors.
FeatureCloudConstPtr	input_features_
	The source point cloud's feature descriptors.
int	k_correspondences_
	The number of neighbors to use when selecting a random feature correspondence.
float	min_sample_distance_
	The minimum distances between samples.
int	nr_samples_
	The number of samples to use during each iteration.
FeatureCloudConstPtr	target_features_
	The target point cloud's feature descriptors.

Detailed Description

template<typename PointSource, typename PointTarget, typename FeatureT>
class pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >

SampleConsensusInitialAlignment is an implementation of the initial alignment algorithm described in section IV of "Fast Point Feature Histograms (FPFH) for 3D Registration," Rusu et al.

Author:: Michael Dixon, Radu B. Rusu

Definition at line 52 of file ia_ransac.h.

Member Typedef Documentation

template<typename PointSource, typename PointTarget, typename FeatureT>

typedef pcl::PointCloud<FeatureT> pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::FeatureCloud

Definition at line 77 of file ia_ransac.h.

template<typename PointSource, typename PointTarget, typename FeatureT>

typedef FeatureCloud::ConstPtr pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::FeatureCloudConstPtr

Definition at line 79 of file ia_ransac.h.

template<typename PointSource, typename PointTarget, typename FeatureT>

typedef FeatureCloud::Ptr pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::FeatureCloudPtr

Definition at line 78 of file ia_ransac.h.

template<typename PointSource, typename PointTarget, typename FeatureT>

typedef KdTreeFLANN<FeatureT>::Ptr pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::FeatureKdTreePtr

Definition at line 125 of file ia_ransac.h.

template<typename PointSource, typename PointTarget, typename FeatureT>

typedef Registration<PointSource, PointTarget>::PointCloudSource pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::PointCloudSource

Reimplemented from pcl::Registration< PointSource, PointTarget >.

Definition at line 68 of file ia_ransac.h.

template<typename PointSource, typename PointTarget, typename FeatureT>

typedef PointCloudSource::ConstPtr pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::PointCloudSourceConstPtr

Reimplemented from pcl::Registration< PointSource, PointTarget >.

Definition at line 70 of file ia_ransac.h.

template<typename PointSource, typename PointTarget, typename FeatureT>

typedef PointCloudSource::Ptr pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::PointCloudSourcePtr

Reimplemented from pcl::Registration< PointSource, PointTarget >.

Definition at line 69 of file ia_ransac.h.

template<typename PointSource, typename PointTarget, typename FeatureT>

typedef Registration<PointSource, PointTarget>::PointCloudTarget pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::PointCloudTarget

Reimplemented from pcl::Registration< PointSource, PointTarget >.

Definition at line 72 of file ia_ransac.h.

template<typename PointSource, typename PointTarget, typename FeatureT>

typedef PointIndices::ConstPtr pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::PointIndicesConstPtr

Reimplemented from pcl::PCLBase< PointSource >.

Definition at line 75 of file ia_ransac.h.

template<typename PointSource, typename PointTarget, typename FeatureT>

typedef PointIndices::Ptr pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::PointIndicesPtr

Reimplemented from pcl::PCLBase< PointSource >.

Definition at line 74 of file ia_ransac.h.

Constructor & Destructor Documentation

template<typename PointSource, typename PointTarget, typename FeatureT>

pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::SampleConsensusInitialAlignment ( ) [inline]

Constructor.

Definition at line 127 of file ia_ransac.h.

Member Function Documentation

template<typename PointSource , typename PointTarget , typename FeatureT >

float pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::computeErrorMetric	(	const PointCloudSource &	cloud,
		float	threshold
	)		`[protected]`

An error metric for that computes the quality of the alignment between the given cloud and the target.

Parameters:

cloud	the input cloud
threshold	distances greater than this value are capped

Definition at line 155 of file ia_ransac.hpp.

template<typename PointSource , typename PointTarget , typename FeatureT >

void pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::computeTransformation	(	PointCloudSource &	output,
		const Eigen::Matrix4f &	guess
	)		`[protected, virtual]`

Rigid transformation computation method.

Parameters:

output the transformed input point cloud dataset using the rigid transformation found

Implements pcl::Registration< PointSource, PointTarget >.

Definition at line 177 of file ia_ransac.hpp.

template<typename PointSource , typename PointTarget , typename FeatureT >

void pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::findSimilarFeatures	(	const FeatureCloud &	input_features,
		const std::vector< int > &	sample_indices,
		std::vector< int > &	corresponding_indices
	)		`[protected]`

For each of the sample points, find a list of points in the target cloud whose features are similar to the sample points' features. From these, select one randomly which will be considered that sample point's correspondence.

Parameters:

input_features	a cloud of feature descriptors
sample_indices	the indices of each sample point
corresponding_indices	the resulting indices of each sample's corresponding point in the target cloud

Definition at line 134 of file ia_ransac.hpp.

template<typename PointSource, typename PointTarget, typename FeatureT>

int pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::getCorrespondenceRandomness ( ) [inline]

Get the number of neighbors used when selecting a random feature correspondence, as set by the user.

Definition at line 187 of file ia_ransac.h.

template<typename PointSource, typename PointTarget, typename FeatureT>

boost::shared_ptr<ErrorFunctor> pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::getErrorFunction ( ) [inline]

Get a shared pointer to the ErrorFunctor that is to be minimized.

Returns:: A shared pointer to a subclass of SampleConsensusInitialAlignment::ErrorFunctor

Definition at line 200 of file ia_ransac.h.

template<typename PointSource, typename PointTarget, typename FeatureT>

float pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::getMinSampleDistance ( ) [inline]

Get the minimum distances between samples, as set by the user.

Definition at line 166 of file ia_ransac.h.

template<typename PointSource, typename PointTarget, typename FeatureT>

int pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::getNumberOfSamples ( ) [inline]

Get the number of samples to use during each iteration, as set by the user.

Definition at line 176 of file ia_ransac.h.

template<typename PointSource, typename PointTarget, typename FeatureT>

int pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::getRandomIndex ( int n ) [inline, protected]

Choose a random index between 0 and n-1.

Parameters:

n	the number of possible indices to choose from

Definition at line 207 of file ia_ransac.h.

template<typename PointSource, typename PointTarget, typename FeatureT>

FeatureCloudConstPtr const pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::getSourceFeatures ( ) [inline]

Get a pointer to the source point cloud's features.

Definition at line 146 of file ia_ransac.h.

template<typename PointSource, typename PointTarget, typename FeatureT>

FeatureCloudConstPtr const pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::getTargetFeatures ( ) [inline]

Get a pointer to the target point cloud's features.

Definition at line 156 of file ia_ransac.h.

template<typename PointSource , typename PointTarget , typename FeatureT >

void pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::selectSamples	(	const PointCloudSource &	cloud,
		int	nr_samples,
		float	min_sample_distance,
		std::vector< int > &	sample_indices
	)		`[protected]`

Select nr_samples sample points from cloud while making sure that their pairwise distances are greater than a user-defined minimum distance, min_sample_distance.

Parameters:

cloud	the input point cloud
nr_samples	the number of samples to select
min_sample_distance	the minimum distance between any two samples
sample_indices	the resulting sample indices

Definition at line 72 of file ia_ransac.hpp.

template<typename PointSource, typename PointTarget, typename FeatureT>

void pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::setCorrespondenceRandomness ( int k ) [inline]

Set the number of neighbors to use when selecting a random feature correspondence. A higher value will add more randomness to the feature matching.

Parameters:

k	the number of neighbors to use when selecting a random feature correspondence.

Definition at line 183 of file ia_ransac.h.

template<typename PointSource, typename PointTarget, typename FeatureT>

void pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::setErrorFunction ( const boost::shared_ptr< ErrorFunctor > & error_functor ) [inline]

Specify the error function to minimize.

Note:: This call is optional. TruncatedError will be used by default

Parameters:

[in] error_functor a shared pointer to a subclass of SampleConsensusInitialAlignment::ErrorFunctor

Definition at line 194 of file ia_ransac.h.

template<typename PointSource, typename PointTarget, typename FeatureT>

void pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::setMinSampleDistance ( float min_sample_distance ) [inline]

Set the minimum distances between samples.

Parameters:

min_sample_distance the minimum distances between samples

Definition at line 162 of file ia_ransac.h.

template<typename PointSource, typename PointTarget, typename FeatureT>

void pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::setNumberOfSamples ( int nr_samples ) [inline]

Set the number of samples to use during each iteration.

Parameters:

nr_samples the number of samples to use during each iteration

Definition at line 172 of file ia_ransac.h.

template<typename PointSource, typename PointTarget, typename FeatureT>

void pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::setSourceFeatures ( const FeatureCloudConstPtr & features )

Provide a boost shared pointer to the source point cloud's feature descriptors.

Parameters:

features the source point cloud's features

Definition at line 47 of file ia_ransac.hpp.

template<typename PointSource, typename PointTarget, typename FeatureT>

void pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::setTargetFeatures ( const FeatureCloudConstPtr & features )

Provide a boost shared pointer to the target point cloud's feature descriptors.

Parameters:

features the target point cloud's features

Definition at line 59 of file ia_ransac.hpp.

Member Data Documentation

template<typename PointSource, typename PointTarget, typename FeatureT>

boost::shared_ptr<ErrorFunctor> pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::error_functor_ [protected]

Definition at line 263 of file ia_ransac.h.

template<typename PointSource, typename PointTarget, typename FeatureT>

FeatureKdTreePtr pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::feature_tree_ [protected]

The KdTree used to compare feature descriptors.

Definition at line 260 of file ia_ransac.h.

template<typename PointSource, typename PointTarget, typename FeatureT>

FeatureCloudConstPtr pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::input_features_ [protected]

The source point cloud's feature descriptors.

Definition at line 245 of file ia_ransac.h.

template<typename PointSource, typename PointTarget, typename FeatureT>

int pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::k_correspondences_ [protected]

The number of neighbors to use when selecting a random feature correspondence.

Definition at line 257 of file ia_ransac.h.

template<typename PointSource, typename PointTarget, typename FeatureT>

float pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::min_sample_distance_ [protected]

The minimum distances between samples.

Definition at line 254 of file ia_ransac.h.

template<typename PointSource, typename PointTarget, typename FeatureT>

int pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::nr_samples_ [protected]

The number of samples to use during each iteration.

Definition at line 251 of file ia_ransac.h.

template<typename PointSource, typename PointTarget, typename FeatureT>

FeatureCloudConstPtr pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::target_features_ [protected]

The target point cloud's feature descriptors.

Definition at line 248 of file ia_ransac.h.

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

Classes

Public Types

Public Member Functions

Protected Member Functions

Protected Attributes

Detailed Description

template<typename PointSource, typename PointTarget, typename FeatureT> class pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >

Member Typedef Documentation

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation

template<typename PointSource, typename PointTarget, typename FeatureT>
class pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >