Public Types | Public Member Functions | Protected Member Functions | Protected Attributes
pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT > Class Template Reference

Pose estimation and alignment class using a prerejective RANSAC routine. More...

#include <sample_consensus_prerejective.h>

Inheritance diagram for pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >:
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List of all members.

Public Types

typedef boost::shared_ptr
< const
SampleConsensusPrerejective
< PointSource, PointTarget,
FeatureT > > 		ConstPtr
typedef
pcl::registration::CorrespondenceRejectorPoly
< PointSource, PointTarget > 		CorrespondenceRejectorPoly
typedef
CorrespondenceRejectorPoly::ConstPtr 		CorrespondenceRejectorPolyConstPtr
typedef
CorrespondenceRejectorPoly::Ptr 		CorrespondenceRejectorPolyPtr
typedef pcl::PointCloud< FeatureT > FeatureCloud
typedef FeatureCloud::ConstPtr FeatureCloudConstPtr
typedef FeatureCloud::Ptr FeatureCloudPtr
typedef KdTreeFLANN< FeatureT >
::Ptr 		FeatureKdTreePtr
typedef Registration
< PointSource, PointTarget >
::Matrix4 		Matrix4
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
typedef boost::shared_ptr
< SampleConsensusPrerejective
< PointSource, PointTarget,
FeatureT > > 		Ptr

Public Member Functions

int getCorrespondenceRandomness () const
 Get the number of neighbors used when selecting a random feature correspondence, as set by the user.
float getInlierFraction () const
 Get the required inlier fraction.
const std::vector< int > & getInliers () const
 Get the inlier indices of the source point cloud under the final transformation.
int getNumberOfSamples () const
 Get the number of samples to use during each iteration, as set by the user.
float getSimilarityThreshold () const
 Get the similarity threshold between edge lengths of the underlying polygonal correspondence rejector object,.
const FeatureCloudConstPtr getSourceFeatures () const
 Get a pointer to the source point cloud's features.
const FeatureCloudConstPtr getTargetFeatures () const
 Get a pointer to the target point cloud's features.
 SampleConsensusPrerejective ()
 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 setInlierFraction (float inlier_fraction)
 Set the required inlier fraction (of the input)
void setNumberOfSamples (int nr_samples)
 Set the number of samples to use during each iteration.
void setSimilarityThreshold (float similarity_threshold)
 Set the similarity threshold in [0,1[ between edge lengths of the underlying polygonal correspondence rejector object, where 1 is a perfect match.
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.
virtual ~SampleConsensusPrerejective ()
 Destructor.

Protected Member Functions

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.
void getFitness (std::vector< int > &inliers, float &fitness_score)
 Obtain the fitness of a transformation The following metrics are calculated, based on final_transformation_ and corr_dist_threshold_:
int getRandomIndex (int n) const
 Choose a random index between 0 and n-1.
void selectSamples (const PointCloudSource &cloud, int nr_samples, 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

CorrespondenceRejectorPolyPtr correspondence_rejector_poly_
 The polygonal correspondence rejector used for prerejection.
FeatureKdTreePtr feature_tree_
 The KdTree used to compare feature descriptors.
float inlier_fraction_
 The fraction [0,1] of inlier points required for accepting a transformation.
std::vector< int > inliers_
 Inlier points of final transformation as indices into source.
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.
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::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >

Pose estimation and alignment class using a prerejective RANSAC routine.

This class inserts a simple, yet effective "prerejection" step into the standard RANSAC pose estimation loop in order to avoid verification of pose hypotheses that are likely to be wrong. This is achieved by local pose-invariant geometric constraints, as also implemented in the class CorrespondenceRejectorPoly.

In order to robustly align partial/occluded models, this routine does not try to minimize the fit error, but instead tries to maximize the inlier rate, above a threshold specifiable using setInlierFraction().

The amount of prerejection or "greedyness" of the algorithm can be specified using setSimilarityThreshold() in [0,1[, where a value of 0 means disabled, and 1 is maximally rejective.

If you use this in academic work, please cite:

A. G. Buch, D. Kraft, J.-K. Kämäräinen, H. G. Petersen and N. Krüger. Pose Estimation using Local Structure-Specific Shape and Appearance Context. International Conference on Robotics and Automation (ICRA), 2013.

Author:
Anders Glent Buch (andersgb1@gmail.com)

Definition at line 77 of file sample_consensus_prerejective.h.

Member Typedef Documentation

template<typename PointSource, typename PointTarget, typename FeatureT>
typedef boost::shared_ptr<const SampleConsensusPrerejective<PointSource, PointTarget, FeatureT> > pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::ConstPtr

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

Definition at line 109 of file sample_consensus_prerejective.h.

template<typename PointSource, typename PointTarget, typename FeatureT>
typedef pcl::registration::CorrespondenceRejectorPoly<PointSource, PointTarget> pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::CorrespondenceRejectorPoly

Definition at line 113 of file sample_consensus_prerejective.h.

template<typename PointSource, typename PointTarget, typename FeatureT>
typedef CorrespondenceRejectorPoly::ConstPtr pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::CorrespondenceRejectorPolyConstPtr

Definition at line 115 of file sample_consensus_prerejective.h.

template<typename PointSource, typename PointTarget, typename FeatureT>
typedef CorrespondenceRejectorPoly::Ptr pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::CorrespondenceRejectorPolyPtr

Definition at line 114 of file sample_consensus_prerejective.h.

template<typename PointSource, typename PointTarget, typename FeatureT>
typedef pcl::PointCloud<FeatureT> pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::FeatureCloud

Definition at line 104 of file sample_consensus_prerejective.h.

template<typename PointSource, typename PointTarget, typename FeatureT>
typedef FeatureCloud::ConstPtr pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::FeatureCloudConstPtr

Definition at line 106 of file sample_consensus_prerejective.h.

template<typename PointSource, typename PointTarget, typename FeatureT>
typedef FeatureCloud::Ptr pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::FeatureCloudPtr

Definition at line 105 of file sample_consensus_prerejective.h.

template<typename PointSource, typename PointTarget, typename FeatureT>
typedef KdTreeFLANN<FeatureT>::Ptr pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::FeatureKdTreePtr

Definition at line 111 of file sample_consensus_prerejective.h.

template<typename PointSource, typename PointTarget, typename FeatureT>
typedef Registration<PointSource, PointTarget>::Matrix4 pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::Matrix4

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

Definition at line 80 of file sample_consensus_prerejective.h.

template<typename PointSource, typename PointTarget, typename FeatureT>
typedef Registration<PointSource, PointTarget>::PointCloudSource pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::PointCloudSource

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

Definition at line 95 of file sample_consensus_prerejective.h.

template<typename PointSource, typename PointTarget, typename FeatureT>
typedef PointCloudSource::ConstPtr pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::PointCloudSourceConstPtr

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

Definition at line 97 of file sample_consensus_prerejective.h.

template<typename PointSource, typename PointTarget, typename FeatureT>
typedef PointCloudSource::Ptr pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::PointCloudSourcePtr

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

Definition at line 96 of file sample_consensus_prerejective.h.

template<typename PointSource, typename PointTarget, typename FeatureT>
typedef Registration<PointSource, PointTarget>::PointCloudTarget pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::PointCloudTarget

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

Definition at line 99 of file sample_consensus_prerejective.h.

template<typename PointSource, typename PointTarget, typename FeatureT>
typedef PointIndices::ConstPtr pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::PointIndicesConstPtr

Reimplemented from pcl::PCLBase< PointSource >.

Definition at line 102 of file sample_consensus_prerejective.h.

template<typename PointSource, typename PointTarget, typename FeatureT>
typedef PointIndices::Ptr pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::PointIndicesPtr

Reimplemented from pcl::PCLBase< PointSource >.

Definition at line 101 of file sample_consensus_prerejective.h.

template<typename PointSource, typename PointTarget, typename FeatureT>
typedef boost::shared_ptr<SampleConsensusPrerejective<PointSource, PointTarget, FeatureT> > pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::Ptr

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

Definition at line 108 of file sample_consensus_prerejective.h.

Constructor & Destructor Documentation

template<typename PointSource, typename PointTarget, typename FeatureT>
pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::SampleConsensusPrerejective ( ) [inline]

Constructor.

Definition at line 118 of file sample_consensus_prerejective.h.

template<typename PointSource, typename PointTarget, typename FeatureT>
virtual pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::~SampleConsensusPrerejective ( ) [inline, virtual]

Destructor.

Definition at line 134 of file sample_consensus_prerejective.h.

Member Function Documentation

template<typename PointSource , typename PointTarget , typename FeatureT >
void pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::computeTransformation ( PointCloudSource &  output,
const Eigen::Matrix4f &  guess 
) [protected]

Rigid transformation computation method.

Parameters:
outputthe transformed input point cloud dataset using the rigid transformation found

Definition at line 134 of file sample_consensus_prerejective.hpp.

template<typename PointSource , typename PointTarget , typename FeatureT >
void pcl::SampleConsensusPrerejective< 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_featuresa cloud of feature descriptors
sample_indicesthe indices of each sample point
corresponding_indicesthe resulting indices of each sample's corresponding point in the target cloud

Definition at line 106 of file sample_consensus_prerejective.hpp.

template<typename PointSource, typename PointTarget, typename FeatureT>
int pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::getCorrespondenceRandomness ( ) const [inline]

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

Definition at line 192 of file sample_consensus_prerejective.h.

template<typename PointSource , typename PointTarget , typename FeatureT >
void pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::getFitness ( std::vector< int > &  inliers,
float &  fitness_score 
) [protected]

Obtain the fitness of a transformation The following metrics are calculated, based on final_transformation_ and corr_dist_threshold_:

  • Inliers: the number of transformed points which are closer than threshold to NN
  • Error score: the MSE of the inliers
    Parameters:
    inliersindices of source point cloud inliers
    fitness_scoreoutput fitness score as RMSE

Definition at line 271 of file sample_consensus_prerejective.hpp.

template<typename PointSource, typename PointTarget, typename FeatureT>
float pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::getInlierFraction ( ) const [inline]

Get the required inlier fraction.

Returns:
required inlier fraction in [0,1]

Definition at line 229 of file sample_consensus_prerejective.h.

template<typename PointSource, typename PointTarget, typename FeatureT>
const std::vector<int>& pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::getInliers ( ) const [inline]

Get the inlier indices of the source point cloud under the final transformation.

Returns:
inlier indices

Definition at line 238 of file sample_consensus_prerejective.h.

template<typename PointSource, typename PointTarget, typename FeatureT>
int pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::getNumberOfSamples ( ) const [inline]

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

Definition at line 175 of file sample_consensus_prerejective.h.

template<typename PointSource, typename PointTarget, typename FeatureT>
int pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::getRandomIndex ( int  n) const [inline, protected]

Choose a random index between 0 and n-1.

Parameters:
nthe number of possible indices to choose from

Definition at line 248 of file sample_consensus_prerejective.h.

template<typename PointSource, typename PointTarget, typename FeatureT>
float pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::getSimilarityThreshold ( ) const [inline]

Get the similarity threshold between edge lengths of the underlying polygonal correspondence rejector object,.

Returns:
edge length similarity threshold

Definition at line 211 of file sample_consensus_prerejective.h.

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

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

Definition at line 146 of file sample_consensus_prerejective.h.

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

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

Definition at line 159 of file sample_consensus_prerejective.h.

template<typename PointSource , typename PointTarget , typename FeatureT >
void pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::selectSamples ( const PointCloudSource &  cloud,
int  nr_samples,
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:
cloudthe input point cloud
nr_samplesthe number of samples to select
sample_indicesthe resulting sample indices

Definition at line 71 of file sample_consensus_prerejective.hpp.

template<typename PointSource, typename PointTarget, typename FeatureT>
void pcl::SampleConsensusPrerejective< 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:
kthe number of neighbors to use when selecting a random feature correspondence.

Definition at line 185 of file sample_consensus_prerejective.h.

template<typename PointSource, typename PointTarget, typename FeatureT>
void pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::setInlierFraction ( float  inlier_fraction) [inline]

Set the required inlier fraction (of the input)

Parameters:
inlier_fractionrequired inlier fraction, must be in [0,1]

Definition at line 220 of file sample_consensus_prerejective.h.

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

Set the number of samples to use during each iteration.

Parameters:
nr_samplesthe number of samples to use during each iteration

Definition at line 168 of file sample_consensus_prerejective.h.

template<typename PointSource, typename PointTarget, typename FeatureT>
void pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::setSimilarityThreshold ( float  similarity_threshold) [inline]

Set the similarity threshold in [0,1[ between edge lengths of the underlying polygonal correspondence rejector object, where 1 is a perfect match.

Parameters:
similarity_thresholdedge length similarity threshold

Definition at line 202 of file sample_consensus_prerejective.h.

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

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

Parameters:
featuresthe source point cloud's features

Definition at line 46 of file sample_consensus_prerejective.hpp.

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

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

Parameters:
featuresthe target point cloud's features

Definition at line 58 of file sample_consensus_prerejective.hpp.

Member Data Documentation

template<typename PointSource, typename PointTarget, typename FeatureT>
CorrespondenceRejectorPolyPtr pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::correspondence_rejector_poly_ [protected]

The polygonal correspondence rejector used for prerejection.

Definition at line 307 of file sample_consensus_prerejective.h.

template<typename PointSource, typename PointTarget, typename FeatureT>
FeatureKdTreePtr pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::feature_tree_ [protected]

The KdTree used to compare feature descriptors.

Definition at line 304 of file sample_consensus_prerejective.h.

template<typename PointSource, typename PointTarget, typename FeatureT>
float pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::inlier_fraction_ [protected]

The fraction [0,1] of inlier points required for accepting a transformation.

Definition at line 310 of file sample_consensus_prerejective.h.

template<typename PointSource, typename PointTarget, typename FeatureT>
std::vector<int> pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::inliers_ [protected]

Inlier points of final transformation as indices into source.

Definition at line 313 of file sample_consensus_prerejective.h.

template<typename PointSource, typename PointTarget, typename FeatureT>
FeatureCloudConstPtr pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::input_features_ [protected]

The source point cloud's feature descriptors.

Definition at line 292 of file sample_consensus_prerejective.h.

template<typename PointSource, typename PointTarget, typename FeatureT>
int pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::k_correspondences_ [protected]

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

Definition at line 301 of file sample_consensus_prerejective.h.

template<typename PointSource, typename PointTarget, typename FeatureT>
int pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::nr_samples_ [protected]

The number of samples to use during each iteration.

Definition at line 298 of file sample_consensus_prerejective.h.

template<typename PointSource, typename PointTarget, typename FeatureT>
FeatureCloudConstPtr pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::target_features_ [protected]

The target point cloud's feature descriptors.

Definition at line 295 of file sample_consensus_prerejective.h.

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

pcl
Author(s): Open Perception
autogenerated on Wed Aug 26 2015 15:43:35