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/PointCloudLibrary

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 <pcl/registration/ia_ransac.h>

Classes

class ErrorFunctor
class HuberPenalty
class TruncatedError

Public Types

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 = shared_ptr< SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT > >
using ConstPtr = shared_ptr< const SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT > >
using ErrorFunctorPtr = typename ErrorFunctor::Ptr
using FeatureKdTreePtr = typename KdTreeFLANN< FeatureT >::Ptr
- Public Types inherited from pcl::Registration< PointSource, PointTarget >
using Matrix4 = Eigen::Matrix< float, 4, 4 >
using Ptr = shared_ptr< Registration< PointSource, PointTarget, float > >
using ConstPtr = 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 pcl::Indices &, const pcl::PointCloud< PointTarget > &, const pcl::Indices &)
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 = PointIndices::Ptr
using PointIndicesConstPtr = PointIndices::ConstPtr

Public Member Functions

SampleConsensusInitialAlignment ()
Constructor. More...
void setSourceFeatures (const FeatureCloudConstPtr &features)
Provide a shared pointer to the source point cloud's feature descriptors. More...
const FeatureCloudConstPtr getSourceFeatures ()
Get a pointer to the source point cloud's features. More...
void setTargetFeatures (const FeatureCloudConstPtr &features)
Provide a shared pointer to the target point cloud's feature descriptors. More...
const FeatureCloudConstPtr getTargetFeatures ()
Get a pointer to the target point cloud's features. More...
void setMinSampleDistance (float min_sample_distance)
Set the minimum distances between samples. More...
float getMinSampleDistance ()
Get the minimum distances between samples, as set by the user. More...
void setNumberOfSamples (int nr_samples)
Set the number of samples to use during each iteration. More...
int getNumberOfSamples ()
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 ()
Get the number of neighbors used when selecting a random feature correspondence, as set by the user. More...
void setErrorFunction (const ErrorFunctorPtr &error_functor)
Specify the error function to minimize. More...
ErrorFunctorPtr getErrorFunction ()
Get a shared pointer to the ErrorFunctor that is to be minimized. 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...
const PointCloudSourceConstPtr 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...
const PointCloudTargetConstPtr 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 () const
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< CorrespondenceRejectorPtr > getCorrespondenceRejectors ()
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 ()=default
Destructor. More...
virtual void setInputCloud (const PointCloudConstPtr &cloud)
Provide a pointer to the input dataset. More...
const PointCloudConstPtr 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 (std::size_t row_start, std::size_t col_start, std::size_t nb_rows, std::size_t nb_cols)
Set the indices for the points laying within an interest region of the point cloud. More...
IndicesPtr getIndices ()
Get a pointer to the vector of indices used. More...
const IndicesConstPtr getIndices () const
Get a pointer to the vector of indices used. More...
const PointSource & operator[] (std::size_t pos) const
Override PointCloud operator[] to shorten code. More...

Protected Member Functions

pcl::index_t getRandomIndex (int n)
Choose a random index between 0 and n-1. More...
void selectSamples (const PointCloudSource &cloud, unsigned int nr_samples, float min_sample_distance, pcl::Indices &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 FeatureCloud &input_features, const pcl::Indices &sample_indices, pcl::Indices &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...
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. More...
void computeTransformation (PointCloudSource &output, const Eigen::Matrix4f &guess) override
Rigid transformation computation method. More...
- Protected Member Functions inherited from pcl::Registration< PointSource, PointTarget >
bool searchForNeighbors (const PointCloudSource &cloud, int index, pcl::Indices &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...
float min_sample_distance_
The minimum distances between samples. 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...
ErrorFunctorPtr error_functor_
- 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< CorrespondenceRejectorPtr > correspondence_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< UpdateVisualizerCallbackSignature > update_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::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 54 of file ia_ransac.h.

Member Typedef Documentation

ConstPtr

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

Definition at line 88 of file ia_ransac.h.

ErrorFunctorPtr

template<typename PointSource , typename PointTarget , typename FeatureT >
using pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::ErrorFunctorPtr = typename ErrorFunctor::Ptr

Definition at line 138 of file ia_ransac.h.

FeatureCloud

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

Definition at line 81 of file ia_ransac.h.

FeatureCloudConstPtr

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

Definition at line 83 of file ia_ransac.h.

FeatureCloudPtr

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

Definition at line 82 of file ia_ransac.h.

FeatureKdTreePtr

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

Definition at line 140 of file ia_ransac.h.

PointCloudSource

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

Definition at line 71 of file ia_ransac.h.

PointCloudSourceConstPtr

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

Definition at line 73 of file ia_ransac.h.

PointCloudSourcePtr

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

Definition at line 72 of file ia_ransac.h.

PointCloudTarget

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

Definition at line 76 of file ia_ransac.h.

PointIndicesConstPtr

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

Definition at line 79 of file ia_ransac.h.

PointIndicesPtr

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

Definition at line 78 of file ia_ransac.h.

Ptr

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

Definition at line 86 of file ia_ransac.h.

Constructor & Destructor Documentation

SampleConsensusInitialAlignment()

Member Function Documentation

computeErrorMetric()

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 166 of file ia_ransac.hpp.

computeTransformation()

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

Rigid transformation computation method.

Parameters
output the transformed input point cloud dataset using the rigid transformation found
guess The computed transforamtion

Definition at line 189 of file ia_ransac.hpp.

References pcl::transformPointCloud().

findSimilarFeatures()

template<typename PointSource , typename PointTarget , typename FeatureT >
void pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::findSimilarFeatures ( const FeatureCloud & input_features,
const pcl::Indices & sample_indices,
pcl::Indices & 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 142 of file ia_ransac.hpp.

getCorrespondenceRandomness()

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 234 of file ia_ransac.h.

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

getErrorFunction()

template<typename PointSource , typename PointTarget , typename FeatureT >
ErrorFunctorPtr 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 255 of file ia_ransac.h.

References pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::error_functor_.

getMinSampleDistance()

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 198 of file ia_ransac.h.

References pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::min_sample_distance_.

getNumberOfSamples()

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 215 of file ia_ransac.h.

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

getRandomIndex()

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

Choose a random index between 0 and n-1.

Parameters
n the number of possible indices to choose from

Definition at line 265 of file ia_ransac.h.

getSourceFeatures()

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

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

Definition at line 169 of file ia_ransac.h.

References pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::input_features_.

getTargetFeatures()

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

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

Definition at line 182 of file ia_ransac.h.

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

selectSamples()

template<typename PointSource , typename PointTarget , typename FeatureT >
void pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::selectSamples ( const PointCloudSource & cloud,
unsigned int nr_samples,
float min_sample_distance,
pcl::Indices & 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 79 of file ia_ransac.hpp.

References pcl::euclideanDistance().

setCorrespondenceRandomness()

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 226 of file ia_ransac.h.

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

setErrorFunction()

template<typename PointSource , typename PointTarget , typename FeatureT >
void pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::setErrorFunction ( const ErrorFunctorPtr & 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 245 of file ia_ransac.h.

References pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::error_functor_.

setMinSampleDistance()

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 191 of file ia_ransac.h.

References pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::min_sample_distance_.

setNumberOfSamples()

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 207 of file ia_ransac.h.

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

setSourceFeatures()

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

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

Parameters
features the source point cloud's features

Definition at line 50 of file ia_ransac.hpp.

setTargetFeatures()

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

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

Parameters
features the target point cloud's features

Definition at line 64 of file ia_ransac.hpp.

Member Data Documentation

error_functor_

template<typename PointSource , typename PointTarget , typename FeatureT >
ErrorFunctorPtr pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::error_functor_
protected

feature_tree_

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 326 of file ia_ransac.h.

input_features_

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 310 of file ia_ransac.h.

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

k_correspondences_

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

min_sample_distance_

template<typename PointSource , typename PointTarget , typename FeatureT >
float pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::min_sample_distance_
protected

nr_samples_

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

target_features_

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 313 of file ia_ransac.h.

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


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

© 2009–2012, Willow Garage, Inc.
© 2012–, Open Perception, Inc.
Licensed under the BSD License.
https://pointclouds.org/documentation/classpcl_1_1_sample_consensus_initial_alignment.html