Point Cloud Library (PCL)  1.9.1-dev
List of all members | 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 <pcl/registration/sample_consensus_prerejective.h>

+ Inheritance diagram for pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >:

Public Types

using Matrix4 = typename Registration< PointSource, PointTarget >::Matrix4
 
using PointCloudSource = typename Registration< PointSource, PointTarget >::PointCloudSource
 
using PointCloudSourcePtr = typename PointCloudSource::Ptr
 
using PointCloudSourceConstPtr = typename PointCloudSource::ConstPtr
 
using PointCloudTarget = typename Registration< PointSource, PointTarget >::PointCloudTarget
 
using PointIndicesPtr = PointIndices::Ptr
 
using PointIndicesConstPtr = PointIndices::ConstPtr
 
using FeatureCloud = pcl::PointCloud< FeatureT >
 
using FeatureCloudPtr = typename FeatureCloud::Ptr
 
using FeatureCloudConstPtr = typename FeatureCloud::ConstPtr
 
using Ptr = boost::shared_ptr< SampleConsensusPrerejective< PointSource, PointTarget, FeatureT > >
 
using ConstPtr = boost::shared_ptr< const SampleConsensusPrerejective< PointSource, PointTarget, FeatureT > >
 
using FeatureKdTreePtr = typename KdTreeFLANN< FeatureT >::Ptr
 
using CorrespondenceRejectorPoly = pcl::registration::CorrespondenceRejectorPoly< PointSource, PointTarget >
 
using CorrespondenceRejectorPolyPtr = typename CorrespondenceRejectorPoly::Ptr
 
using CorrespondenceRejectorPolyConstPtr = typename CorrespondenceRejectorPoly::ConstPtr
 
- Public Types inherited from pcl::Registration< PointSource, PointTarget >
using Matrix4 = Eigen::Matrix< float, 4, 4 >
 
using Ptr = boost::shared_ptr< Registration< PointSource, PointTarget, float > >
 
using ConstPtr = boost::shared_ptr< const Registration< PointSource, PointTarget, float > >
 
using CorrespondenceRejectorPtr = pcl::registration::CorrespondenceRejector::Ptr
 
using KdTree = pcl::search::KdTree< PointTarget >
 
using KdTreePtr = typename KdTree::Ptr
 
using KdTreeReciprocal = pcl::search::KdTree< PointSource >
 
using KdTreeReciprocalPtr = typename KdTreeReciprocal::Ptr
 
using PointCloudSource = pcl::PointCloud< PointSource >
 
using PointCloudSourcePtr = typename PointCloudSource::Ptr
 
using PointCloudSourceConstPtr = typename PointCloudSource::ConstPtr
 
using PointCloudTarget = pcl::PointCloud< PointTarget >
 
using PointCloudTargetPtr = typename PointCloudTarget::Ptr
 
using PointCloudTargetConstPtr = typename PointCloudTarget::ConstPtr
 
using PointRepresentationConstPtr = typename KdTree::PointRepresentationConstPtr
 
using TransformationEstimation = typename pcl::registration::TransformationEstimation< PointSource, PointTarget, float >
 
using TransformationEstimationPtr = typename TransformationEstimation::Ptr
 
using TransformationEstimationConstPtr = typename TransformationEstimation::ConstPtr
 
using CorrespondenceEstimation = pcl::registration::CorrespondenceEstimationBase< PointSource, PointTarget, float >
 
using CorrespondenceEstimationPtr = typename CorrespondenceEstimation::Ptr
 
using CorrespondenceEstimationConstPtr = typename CorrespondenceEstimation::ConstPtr
 
using UpdateVisualizerCallbackSignature = void(const pcl::PointCloud< PointSource > &, const std::vector< int > &, const pcl::PointCloud< PointTarget > &, const std::vector< int > &)
 The callback signature to the function updating intermediate source point cloud position during it's registration to the target point cloud. More...
 
- Public Types inherited from pcl::PCLBase< PointSource >
using PointCloud = pcl::PointCloud< PointSource >
 
using PointCloudPtr = typename PointCloud::Ptr
 
using PointCloudConstPtr = typename PointCloud::ConstPtr
 
using PointIndicesPtr = boost::shared_ptr< PointIndices >
 
using PointIndicesConstPtr = boost::shared_ptr< PointIndices const >
 

Public Member Functions

 SampleConsensusPrerejective ()
 Constructor. More...
 
 ~SampleConsensusPrerejective ()
 Destructor. More...
 
void setSourceFeatures (const FeatureCloudConstPtr &features)
 Provide a boost shared pointer to the source point cloud's feature descriptors. More...
 
const FeatureCloudConstPtr getSourceFeatures () const
 Get a pointer to the source point cloud's features. More...
 
void setTargetFeatures (const FeatureCloudConstPtr &features)
 Provide a boost shared pointer to the target point cloud's feature descriptors. More...
 
const FeatureCloudConstPtr getTargetFeatures () const
 Get a pointer to the target point cloud's features. More...
 
void setNumberOfSamples (int nr_samples)
 Set the number of samples to use during each iteration. More...
 
int getNumberOfSamples () const
 Get the number of samples to use during each iteration, as set by the user. More...
 
void setCorrespondenceRandomness (int k)
 Set the number of neighbors to use when selecting a random feature correspondence. More...
 
int getCorrespondenceRandomness () const
 Get the number of neighbors used when selecting a random feature correspondence, as set by the user. More...
 
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. More...
 
float getSimilarityThreshold () const
 Get the similarity threshold between edge lengths of the underlying polygonal correspondence rejector object,. More...
 
void setInlierFraction (float inlier_fraction)
 Set the required inlier fraction (of the input) More...
 
float getInlierFraction () const
 Get the required inlier fraction. More...
 
const std::vector< int > & getInliers () const
 Get the inlier indices of the source point cloud under the final transformation. More...
 
- Public Member Functions inherited from pcl::Registration< PointSource, PointTarget >
 Registration ()
 Empty constructor. More...
 
 ~Registration ()
 destructor. More...
 
void setTransformationEstimation (const TransformationEstimationPtr &te)
 Provide a pointer to the transformation estimation object. More...
 
void setCorrespondenceEstimation (const CorrespondenceEstimationPtr &ce)
 Provide a pointer to the correspondence estimation object. More...
 
virtual void setInputSource (const PointCloudSourceConstPtr &cloud)
 Provide a pointer to the input source (e.g., the point cloud that we want to align to the target) More...
 
PointCloudSourceConstPtr const getInputSource ()
 Get a pointer to the input point cloud dataset target. More...
 
virtual void setInputTarget (const PointCloudTargetConstPtr &cloud)
 Provide a pointer to the input target (e.g., the point cloud that we want to align the input source to) More...
 
PointCloudTargetConstPtr const getInputTarget ()
 Get a pointer to the input point cloud dataset target. More...
 
void setSearchMethodTarget (const KdTreePtr &tree, bool force_no_recompute=false)
 Provide a pointer to the search object used to find correspondences in the target cloud. More...
 
KdTreePtr getSearchMethodTarget () const
 Get a pointer to the search method used to find correspondences in the target cloud. More...
 
void setSearchMethodSource (const KdTreeReciprocalPtr &tree, bool force_no_recompute=false)
 Provide a pointer to the search object used to find correspondences in the source cloud (usually used by reciprocal correspondence finding). More...
 
KdTreeReciprocalPtr getSearchMethodSource () const
 Get a pointer to the search method used to find correspondences in the source cloud. More...
 
Matrix4 getFinalTransformation ()
 Get the final transformation matrix estimated by the registration method. More...
 
Matrix4 getLastIncrementalTransformation ()
 Get the last incremental transformation matrix estimated by the registration method. More...
 
void setMaximumIterations (int nr_iterations)
 Set the maximum number of iterations the internal optimization should run for. More...
 
int getMaximumIterations ()
 Get the maximum number of iterations the internal optimization should run for, as set by the user. More...
 
void setRANSACIterations (int ransac_iterations)
 Set the number of iterations RANSAC should run for. More...
 
double getRANSACIterations ()
 Get the number of iterations RANSAC should run for, as set by the user. More...
 
void setRANSACOutlierRejectionThreshold (double inlier_threshold)
 Set the inlier distance threshold for the internal RANSAC outlier rejection loop. More...
 
double getRANSACOutlierRejectionThreshold ()
 Get the inlier distance threshold for the internal outlier rejection loop as set by the user. More...
 
void setMaxCorrespondenceDistance (double distance_threshold)
 Set the maximum distance threshold between two correspondent points in source <-> target. More...
 
double getMaxCorrespondenceDistance ()
 Get the maximum distance threshold between two correspondent points in source <-> target. More...
 
void setTransformationEpsilon (double epsilon)
 Set the transformation epsilon (maximum allowable translation squared difference between two consecutive transformations) in order for an optimization to be considered as having converged to the final solution. More...
 
double getTransformationEpsilon ()
 Get the transformation epsilon (maximum allowable translation squared difference between two consecutive transformations) as set by the user. More...
 
void setTransformationRotationEpsilon (double epsilon)
 Set the transformation rotation epsilon (maximum allowable rotation difference between two consecutive transformations) in order for an optimization to be considered as having converged to the final solution. More...
 
double getTransformationRotationEpsilon ()
 Get the transformation rotation epsilon (maximum allowable difference between two consecutive transformations) as set by the user (epsilon is the cos(angle) in a axis-angle representation). More...
 
void setEuclideanFitnessEpsilon (double epsilon)
 Set the maximum allowed Euclidean error between two consecutive steps in the ICP loop, before the algorithm is considered to have converged. More...
 
double getEuclideanFitnessEpsilon ()
 Get the maximum allowed distance error before the algorithm will be considered to have converged, as set by the user. More...
 
void setPointRepresentation (const PointRepresentationConstPtr &point_representation)
 Provide a boost shared pointer to the PointRepresentation to be used when comparing points. More...
 
bool registerVisualizationCallback (std::function< UpdateVisualizerCallbackSignature > &visualizerCallback)
 Register the user callback function which will be called from registration thread in order to update point cloud obtained after each iteration. More...
 
double getFitnessScore (double max_range=std::numeric_limits< double >::max())
 Obtain the Euclidean fitness score (e.g., mean of squared distances from the source to the target) More...
 
double getFitnessScore (const std::vector< float > &distances_a, const std::vector< float > &distances_b)
 Obtain the Euclidean fitness score (e.g., mean of squared distances from the source to the target) from two sets of correspondence distances (distances between source and target points) More...
 
bool hasConverged ()
 Return the state of convergence after the last align run. More...
 
void align (PointCloudSource &output)
 Call the registration algorithm which estimates the transformation and returns the transformed source (input) as output. More...
 
void align (PointCloudSource &output, const Matrix4 &guess)
 Call the registration algorithm which estimates the transformation and returns the transformed source (input) as output. More...
 
const std::string & getClassName () const
 Abstract class get name method. More...
 
bool initCompute ()
 Internal computation initialization. More...
 
bool initComputeReciprocal ()
 Internal computation when reciprocal lookup is needed. More...
 
void addCorrespondenceRejector (const CorrespondenceRejectorPtr &rejector)
 Add a new correspondence rejector to the list. More...
 
std::vector< CorrespondenceRejectorPtrgetCorrespondenceRejectors ()
 Get the list of correspondence rejectors. More...
 
bool removeCorrespondenceRejector (unsigned int i)
 Remove the i-th correspondence rejector in the list. More...
 
void clearCorrespondenceRejectors ()
 Clear the list of correspondence rejectors. More...
 
- Public Member Functions inherited from pcl::PCLBase< PointSource >
 PCLBase ()
 Empty constructor. More...
 
 PCLBase (const PCLBase &base)
 Copy constructor. More...
 
virtual ~PCLBase ()
 Destructor. More...
 
virtual void setInputCloud (const PointCloudConstPtr &cloud)
 Provide a pointer to the input dataset. More...
 
PointCloudConstPtr const getInputCloud () const
 Get a pointer to the input point cloud dataset. More...
 
virtual void setIndices (const IndicesPtr &indices)
 Provide a pointer to the vector of indices that represents the input data. More...
 
virtual void setIndices (const IndicesConstPtr &indices)
 Provide a pointer to the vector of indices that represents the input data. More...
 
virtual void setIndices (const PointIndicesConstPtr &indices)
 Provide a pointer to the vector of indices that represents the input data. More...
 
virtual void setIndices (size_t row_start, size_t col_start, size_t nb_rows, size_t nb_cols)
 Set the indices for the points laying within an interest region of the point cloud. More...
 
IndicesPtr const getIndices ()
 Get a pointer to the vector of indices used. More...
 
IndicesConstPtr const getIndices () const
 Get a pointer to the vector of indices used. More...
 
const PointSource & operator[] (size_t pos) const
 Override PointCloud operator[] to shorten code. More...
 

Protected Member Functions

int getRandomIndex (int n) const
 Choose a random index between 0 and n-1. More...
 
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. More...
 
void findSimilarFeatures (const std::vector< int > &sample_indices, std::vector< std::vector< int > > &similar_features, 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. More...
 
void computeTransformation (PointCloudSource &output, const Eigen::Matrix4f &guess) override
 Rigid transformation computation method. More...
 
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_: More...
 
- Protected Member Functions inherited from pcl::Registration< PointSource, PointTarget >
bool searchForNeighbors (const PointCloudSource &cloud, int index, std::vector< int > &indices, std::vector< float > &distances)
 Search for the closest nearest neighbor of a given point. More...
 
virtual void computeTransformation (PointCloudSource &output, const Matrix4 &guess)=0
 Abstract transformation computation method with initial guess. More...
 
- Protected Member Functions inherited from pcl::PCLBase< PointSource >
bool initCompute ()
 This method should get called before starting the actual computation. More...
 
bool deinitCompute ()
 This method should get called after finishing the actual computation. More...
 

Protected Attributes

FeatureCloudConstPtr input_features_
 The source point cloud's feature descriptors. More...
 
FeatureCloudConstPtr target_features_
 The target point cloud's feature descriptors. More...
 
int nr_samples_
 The number of samples to use during each iteration. More...
 
int k_correspondences_
 The number of neighbors to use when selecting a random feature correspondence. More...
 
FeatureKdTreePtr feature_tree_
 The KdTree used to compare feature descriptors. More...
 
CorrespondenceRejectorPolyPtr correspondence_rejector_poly_
 The polygonal correspondence rejector used for prerejection. More...
 
float inlier_fraction_
 The fraction [0,1] of inlier points required for accepting a transformation. More...
 
std::vector< int > inliers_
 Inlier points of final transformation as indices into source. More...
 
- Protected Attributes inherited from pcl::Registration< PointSource, PointTarget >
std::string reg_name_
 The registration method name. More...
 
KdTreePtr tree_
 A pointer to the spatial search object. More...
 
KdTreeReciprocalPtr tree_reciprocal_
 A pointer to the spatial search object of the source. More...
 
int nr_iterations_
 The number of iterations the internal optimization ran for (used internally). More...
 
int max_iterations_
 The maximum number of iterations the internal optimization should run for. More...
 
int ransac_iterations_
 The number of iterations RANSAC should run for. More...
 
PointCloudTargetConstPtr target_
 The input point cloud dataset target. More...
 
Matrix4 final_transformation_
 The final transformation matrix estimated by the registration method after N iterations. More...
 
Matrix4 transformation_
 The transformation matrix estimated by the registration method. More...
 
Matrix4 previous_transformation_
 The previous transformation matrix estimated by the registration method (used internally). More...
 
double transformation_epsilon_
 The maximum difference between two consecutive transformations in order to consider convergence (user defined). More...
 
double transformation_rotation_epsilon_
 The maximum rotation difference between two consecutive transformations in order to consider convergence (user defined). More...
 
double euclidean_fitness_epsilon_
 The maximum allowed Euclidean error between two consecutive steps in the ICP loop, before the algorithm is considered to have converged. More...
 
double corr_dist_threshold_
 The maximum distance threshold between two correspondent points in source <-> target. More...
 
double inlier_threshold_
 The inlier distance threshold for the internal RANSAC outlier rejection loop. More...
 
bool converged_
 Holds internal convergence state, given user parameters. More...
 
int min_number_correspondences_
 The minimum number of correspondences that the algorithm needs before attempting to estimate the transformation. More...
 
CorrespondencesPtr correspondences_
 The set of correspondences determined at this ICP step. More...
 
TransformationEstimationPtr transformation_estimation_
 A TransformationEstimation object, used to calculate the 4x4 rigid transformation. More...
 
CorrespondenceEstimationPtr correspondence_estimation_
 A CorrespondenceEstimation object, used to estimate correspondences between the source and the target cloud. More...
 
std::vector< CorrespondenceRejectorPtrcorrespondence_rejectors_
 The list of correspondence rejectors to use. More...
 
bool target_cloud_updated_
 Variable that stores whether we have a new target cloud, meaning we need to pre-process it again. More...
 
bool source_cloud_updated_
 Variable that stores whether we have a new source cloud, meaning we need to pre-process it again. More...
 
bool force_no_recompute_
 A flag which, if set, means the tree operating on the target cloud will never be recomputed. More...
 
bool force_no_recompute_reciprocal_
 A flag which, if set, means the tree operating on the source cloud will never be recomputed. More...
 
std::function< UpdateVisualizerCallbackSignatureupdate_visualizer_
 Callback function to update intermediate source point cloud position during it's registration to the target point cloud. More...
 
- Protected Attributes inherited from pcl::PCLBase< PointSource >
PointCloudConstPtr input_
 The input point cloud dataset. More...
 
IndicesPtr indices_
 A pointer to the vector of point indices to use. More...
 
bool use_indices_
 Set to true if point indices are used. More...
 
bool fake_indices_
 If no set of indices are given, we construct a set of fake indices that mimic the input PointCloud. More...
 

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 performs fit error evaluation using only inliers, i.e. points closer than a Euclidean threshold, which is 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 (ander.nosp@m.sgb1.nosp@m.@gmai.nosp@m.l.co.nosp@m.m)

Definition at line 76 of file sample_consensus_prerejective.h.

Member Typedef Documentation

◆ ConstPtr

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

Definition at line 108 of file sample_consensus_prerejective.h.

◆ CorrespondenceRejectorPoly

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

Definition at line 112 of file sample_consensus_prerejective.h.

◆ CorrespondenceRejectorPolyConstPtr

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

Definition at line 114 of file sample_consensus_prerejective.h.

◆ CorrespondenceRejectorPolyPtr

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

Definition at line 113 of file sample_consensus_prerejective.h.

◆ FeatureCloud

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

Definition at line 103 of file sample_consensus_prerejective.h.

◆ FeatureCloudConstPtr

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

Definition at line 105 of file sample_consensus_prerejective.h.

◆ FeatureCloudPtr

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

Definition at line 104 of file sample_consensus_prerejective.h.

◆ FeatureKdTreePtr

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

Definition at line 110 of file sample_consensus_prerejective.h.

◆ Matrix4

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

Definition at line 79 of file sample_consensus_prerejective.h.

◆ PointCloudSource

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

Definition at line 94 of file sample_consensus_prerejective.h.

◆ PointCloudSourceConstPtr

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

Definition at line 96 of file sample_consensus_prerejective.h.

◆ PointCloudSourcePtr

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

Definition at line 95 of file sample_consensus_prerejective.h.

◆ PointCloudTarget

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

Definition at line 98 of file sample_consensus_prerejective.h.

◆ PointIndicesConstPtr

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

Definition at line 101 of file sample_consensus_prerejective.h.

◆ PointIndicesPtr

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

Definition at line 100 of file sample_consensus_prerejective.h.

◆ Ptr

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

Definition at line 107 of file sample_consensus_prerejective.h.

Constructor & Destructor Documentation

◆ SampleConsensusPrerejective()

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

◆ ~SampleConsensusPrerejective()

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

Member Function Documentation

◆ computeTransformation()

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

Rigid transformation computation method.

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

Definition at line 144 of file sample_consensus_prerejective.hpp.

References pcl::transformPointCloud().

Referenced by pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::getRandomIndex().

◆ findSimilarFeatures()

template<typename PointSource , typename PointTarget , typename FeatureT >
void pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::findSimilarFeatures ( const std::vector< int > &  sample_indices,
std::vector< std::vector< int > > &  similar_features,
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
sample_indicesthe indices of each sample point
similar_featurescorrespondence cache, which is used to read/write already computed correspondences
corresponding_indicesthe resulting indices of each sample's corresponding point in the target cloud

Definition at line 115 of file sample_consensus_prerejective.hpp.

Referenced by pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::getRandomIndex().

◆ getCorrespondenceRandomness()

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 191 of file sample_consensus_prerejective.h.

References pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::k_correspondences_.

◆ getFitness()

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 294 of file sample_consensus_prerejective.hpp.

References pcl::transformPointCloud().

Referenced by pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::getRandomIndex().

◆ getInlierFraction()

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 228 of file sample_consensus_prerejective.h.

References pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::inlier_fraction_.

◆ getInliers()

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 237 of file sample_consensus_prerejective.h.

References pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::inliers_.

◆ getNumberOfSamples()

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 174 of file sample_consensus_prerejective.h.

References pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::nr_samples_.

◆ getRandomIndex()

template<typename PointSource , typename PointTarget , typename FeatureT >
int pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::getRandomIndex ( int  n) const
inlineprotected

◆ getSimilarityThreshold()

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 210 of file sample_consensus_prerejective.h.

References pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::correspondence_rejector_poly_.

◆ getSourceFeatures()

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

◆ getTargetFeatures()

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 158 of file sample_consensus_prerejective.h.

References pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::target_features_.

◆ selectSamples()

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.

Referenced by pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::getRandomIndex().

◆ setCorrespondenceRandomness()

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 184 of file sample_consensus_prerejective.h.

References pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::k_correspondences_.

◆ setInlierFraction()

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 219 of file sample_consensus_prerejective.h.

References pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::inlier_fraction_.

◆ setNumberOfSamples()

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 167 of file sample_consensus_prerejective.h.

References pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::nr_samples_.

◆ setSimilarityThreshold()

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 201 of file sample_consensus_prerejective.h.

References pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::correspondence_rejector_poly_.

◆ setSourceFeatures()

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.

Referenced by pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::~SampleConsensusPrerejective().

◆ setTargetFeatures()

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.

Referenced by pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::getSourceFeatures().

Member Data Documentation

◆ correspondence_rejector_poly_

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

◆ feature_tree_

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.

◆ inlier_fraction_

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

◆ inliers_

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.

Referenced by pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::getInliers().

◆ input_features_

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.

Referenced by pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::getSourceFeatures().

◆ k_correspondences_

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

◆ nr_samples_

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

◆ target_features_

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.

Referenced by pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::getTargetFeatures().


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