Point Cloud Library (PCL)  1.14.0-dev
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pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar > Class Template Reference

GeneralizedIterativeClosestPoint is an ICP variant that implements the generalized iterative closest point algorithm as described by Alex Segal et al. More...

#include <pcl/registration/gicp.h>

+ Inheritance diagram for pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >:
+ Collaboration diagram for pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >:

Classes

struct  OptimizationFunctorWithIndices
 optimization functor structure More...
 

Public Types

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 PointIndicesPtr = PointIndices::Ptr
 
using PointIndicesConstPtr = PointIndices::ConstPtr
 
using MatricesVector = std::vector< Eigen::Matrix3d, Eigen::aligned_allocator< Eigen::Matrix3d > >
 
using MatricesVectorPtr = shared_ptr< MatricesVector >
 
using MatricesVectorConstPtr = shared_ptr< const MatricesVector >
 
using InputKdTree = typename Registration< PointSource, PointTarget, Scalar >::KdTree
 
using InputKdTreePtr = typename Registration< PointSource, PointTarget, Scalar >::KdTreePtr
 
using Ptr = shared_ptr< GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar > >
 
using ConstPtr = shared_ptr< const GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar > >
 
using Vector3 = typename Eigen::Matrix< Scalar, 3, 1 >
 
using Vector4 = typename Eigen::Matrix< Scalar, 4, 1 >
 
using Vector6d = Eigen::Matrix< double, 6, 1 >
 
using Matrix3 = typename Eigen::Matrix< Scalar, 3, 3 >
 
using Matrix4 = typename IterativeClosestPoint< PointSource, PointTarget, Scalar >::Matrix4
 
using Matrix6d = Eigen::Matrix< double, 6, 6 >
 
using AngleAxis = typename Eigen::AngleAxis< Scalar >
 
- Public Types inherited from pcl::IterativeClosestPoint< PointSource, PointTarget, float >
using PointCloudSource = typename Registration< PointSource, PointTarget, float >::PointCloudSource
 
using PointCloudSourcePtr = typename PointCloudSource::Ptr
 
using PointCloudSourceConstPtr = typename PointCloudSource::ConstPtr
 
using PointCloudTarget = typename Registration< PointSource, PointTarget, float >::PointCloudTarget
 
using PointCloudTargetPtr = typename PointCloudTarget::Ptr
 
using PointCloudTargetConstPtr = typename PointCloudTarget::ConstPtr
 
using PointIndicesPtr = PointIndices::Ptr
 
using PointIndicesConstPtr = PointIndices::ConstPtr
 
using Ptr = shared_ptr< IterativeClosestPoint< PointSource, PointTarget, float > >
 
using ConstPtr = shared_ptr< const IterativeClosestPoint< PointSource, PointTarget, float > >
 
using Matrix4 = typename Registration< PointSource, PointTarget, float >::Matrix4
 
- Public Types inherited from pcl::Registration< PointSource, PointTarget, float >
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

PCL_MAKE_ALIGNED_OPERATOR_NEW GeneralizedIterativeClosestPoint ()
 Empty constructor. More...
 
void setInputSource (const PointCloudSourceConstPtr &cloud) override
 Provide a pointer to the input dataset. More...
 
void setSourceCovariances (const MatricesVectorPtr &covariances)
 Provide a pointer to the covariances of the input source (if computed externally!). More...
 
void setInputTarget (const PointCloudTargetConstPtr &target) override
 Provide a pointer to the input target (e.g., the point cloud that we want to align the input source to) More...
 
void setTargetCovariances (const MatricesVectorPtr &covariances)
 Provide a pointer to the covariances of the input target (if computed externally!). More...
 
void estimateRigidTransformationBFGS (const PointCloudSource &cloud_src, const pcl::Indices &indices_src, const PointCloudTarget &cloud_tgt, const pcl::Indices &indices_tgt, Matrix4 &transformation_matrix)
 Estimate a rigid rotation transformation between a source and a target point cloud using an iterative non-linear BFGS approach. More...
 
void estimateRigidTransformationNewton (const PointCloudSource &cloud_src, const pcl::Indices &indices_src, const PointCloudTarget &cloud_tgt, const pcl::Indices &indices_tgt, Matrix4 &transformation_matrix)
 Estimate a rigid rotation transformation between a source and a target point cloud using an iterative non-linear Newton approach. More...
 
const Eigen::Matrix3d & mahalanobis (std::size_t index) const
 
void computeRDerivative (const Vector6d &x, const Eigen::Matrix3d &dCost_dR_T, Vector6d &g) const
 Computes the derivative of the cost function w.r.t rotation angles. More...
 
void setRotationEpsilon (double epsilon)
 Set the rotation epsilon (maximum allowable difference between two consecutive rotations) in order for an optimization to be considered as having converged to the final solution. More...
 
double getRotationEpsilon () const
 Get the rotation epsilon (maximum allowable difference between two consecutive rotations) as set by the user. More...
 
void setCorrespondenceRandomness (int k)
 Set the number of neighbors used when selecting a point neighbourhood to compute covariances. More...
 
int getCorrespondenceRandomness () const
 Get the number of neighbors used when computing covariances as set by the user. More...
 
void useBFGS ()
 Use BFGS optimizer instead of default Newton optimizer. More...
 
void setMaximumOptimizerIterations (int max)
 Set maximum number of iterations at the optimization step. More...
 
int getMaximumOptimizerIterations () const
 Return maximum number of iterations at the optimization step. More...
 
void setTranslationGradientTolerance (double tolerance)
 Set the minimal translation gradient threshold for early optimization stop. More...
 
double getTranslationGradientTolerance () const
 Return the minimal translation gradient threshold for early optimization stop. More...
 
void setRotationGradientTolerance (double tolerance)
 Set the minimal rotation gradient threshold for early optimization stop. More...
 
double getRotationGradientTolerance () const
 Return the minimal rotation gradient threshold for early optimization stop. More...
 
- Public Member Functions inherited from pcl::IterativeClosestPoint< PointSource, PointTarget, float >
 IterativeClosestPoint ()
 Empty constructor. More...
 
 IterativeClosestPoint (const IterativeClosestPoint &)=delete
 Due to convergence_criteria_ holding references to the class members, it is tricky to correctly implement its copy and move operations correctly. More...
 
 IterativeClosestPoint (IterativeClosestPoint &&)=delete
 
IterativeClosestPointoperator= (const IterativeClosestPoint &)=delete
 
IterativeClosestPointoperator= (IterativeClosestPoint &&)=delete
 
 ~IterativeClosestPoint () override=default
 Empty destructor. More...
 
pcl::registration::DefaultConvergenceCriteria< float >::Ptr getConvergeCriteria ()
 Returns a pointer to the DefaultConvergenceCriteria used by the IterativeClosestPoint class. More...
 
void setInputSource (const PointCloudSourceConstPtr &cloud) override
 Provide a pointer to the input source (e.g., the point cloud that we want to align to the target) More...
 
void setInputTarget (const PointCloudTargetConstPtr &cloud) override
 Provide a pointer to the input target (e.g., the point cloud that we want to align the input source to) More...
 
void setUseReciprocalCorrespondences (bool use_reciprocal_correspondence)
 Set whether to use reciprocal correspondence or not. More...
 
bool getUseReciprocalCorrespondences () const
 Obtain whether reciprocal correspondence are used or not. More...
 
- Public Member Functions inherited from pcl::Registration< PointSource, PointTarget, float >
 Registration ()
 Empty constructor. More...
 
 ~Registration () override=default
 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...
 
PointCloudSourceConstPtr const getInputSource ()
 Get a pointer to the input point cloud dataset target. 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 () 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< 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 ()=default
 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 (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...
 
IndicesConstPtr const 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

template<typename PointT >
void computeCovariances (typename pcl::PointCloud< PointT >::ConstPtr cloud, const typename pcl::search::KdTree< PointT >::Ptr tree, MatricesVector &cloud_covariances)
 compute points covariances matrices according to the K nearest neighbors. More...
 
double matricesInnerProd (const Eigen::MatrixXd &mat1, const Eigen::MatrixXd &mat2) const
 
void computeTransformation (PointCloudSource &output, const Matrix4 &guess) override
 Rigid transformation computation method with initial guess. More...
 
bool searchForNeighbors (const PointSource &query, pcl::Indices &index, std::vector< float > &distance)
 Search for the closest nearest neighbor of a given point. More...
 
void applyState (Matrix4 &t, const Vector6d &x) const
 compute transformation matrix from transformation matrix More...
 
- Protected Member Functions inherited from pcl::IterativeClosestPoint< PointSource, PointTarget, float >
virtual void transformCloud (const PointCloudSource &input, PointCloudSource &output, const Matrix4 &transform)
 Apply a rigid transform to a given dataset. More...
 
void computeTransformation (PointCloudSource &output, const Matrix4 &guess) override
 Rigid transformation computation method with initial guess. More...
 
virtual void determineRequiredBlobData ()
 Looks at the Estimators and Rejectors and determines whether their blob-setter methods need to be called. More...
 
- Protected Member Functions inherited from pcl::Registration< PointSource, PointTarget, float >
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

int k_correspondences_ {20}
 The number of neighbors used for covariances computation. More...
 
double gicp_epsilon_ {0.001}
 The epsilon constant for gicp paper; this is NOT the convergence tolerance default: 0.001. More...
 
double rotation_epsilon_ {2e-3}
 The epsilon constant for rotation error. More...
 
Matrix4 base_transformation_
 base transformation More...
 
const PointCloudSourcetmp_src_
 Temporary pointer to the source dataset. More...
 
const PointCloudTargettmp_tgt_
 Temporary pointer to the target dataset. More...
 
const pcl::Indicestmp_idx_src_
 Temporary pointer to the source dataset indices. More...
 
const pcl::Indicestmp_idx_tgt_
 Temporary pointer to the target dataset indices. More...
 
MatricesVectorPtr input_covariances_
 Input cloud points covariances. More...
 
MatricesVectorPtr target_covariances_
 Target cloud points covariances. More...
 
std::vector< Eigen::Matrix3d > mahalanobis_
 Mahalanobis matrices holder. More...
 
int max_inner_iterations_ {20}
 maximum number of optimizations More...
 
double translation_gradient_tolerance_ {1e-2}
 minimal translation gradient for early optimization stop More...
 
double rotation_gradient_tolerance_ {1e-2}
 minimal rotation gradient for early optimization stop More...
 
std::function< void(const pcl::PointCloud< PointSource > &cloud_src, const pcl::Indices &src_indices, const pcl::PointCloud< PointTarget > &cloud_tgt, const pcl::Indices &tgt_indices, Matrix4 &transformation_matrix)> rigid_transformation_estimation_
 
- Protected Attributes inherited from pcl::IterativeClosestPoint< PointSource, PointTarget, float >
std::size_t x_idx_offset_
 XYZ fields offset. More...
 
std::size_t y_idx_offset_
 
std::size_t z_idx_offset_
 
std::size_t nx_idx_offset_
 Normal fields offset. More...
 
std::size_t ny_idx_offset_
 
std::size_t nz_idx_offset_
 
bool use_reciprocal_correspondence_
 The correspondence type used for correspondence estimation. More...
 
bool source_has_normals_
 Internal check whether source dataset has normals or not. More...
 
bool target_has_normals_
 Internal check whether target dataset has normals or not. More...
 
bool need_source_blob_
 Checks for whether estimators and rejectors need various data. More...
 
bool need_target_blob_
 
- Protected Attributes inherited from pcl::Registration< PointSource, PointTarget, float >
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...
 
unsigned 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...
 

Additional Inherited Members

- Public Attributes inherited from pcl::IterativeClosestPoint< PointSource, PointTarget, float >
pcl::registration::DefaultConvergenceCriteria< float >::Ptr convergence_criteria_
 

Detailed Description

template<typename PointSource, typename PointTarget, typename Scalar = float>
class pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >

GeneralizedIterativeClosestPoint is an ICP variant that implements the generalized iterative closest point algorithm as described by Alex Segal et al.

in http://www.robots.ox.ac.uk/~avsegal/resources/papers/Generalized_ICP.pdf The approach is based on using anisotropic cost functions to optimize the alignment after closest point assignments have been made. The original code uses GSL and ANN while in ours we use FLANN and Newton's method for optimization (call useBFGS to switch to BFGS optimizer, however Newton is usually faster and more accurate).

Author
Nizar Sallem

Definition at line 59 of file gicp.h.

Member Typedef Documentation

◆ AngleAxis

template<typename PointSource , typename PointTarget , typename Scalar = float>
using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::AngleAxis = typename Eigen::AngleAxis<Scalar>

Definition at line 116 of file gicp.h.

◆ ConstPtr

template<typename PointSource , typename PointTarget , typename Scalar = float>
using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::ConstPtr = shared_ptr< const GeneralizedIterativeClosestPoint<PointSource, PointTarget, Scalar> >

Definition at line 106 of file gicp.h.

◆ InputKdTree

template<typename PointSource , typename PointTarget , typename Scalar = float>
using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::InputKdTree = typename Registration<PointSource, PointTarget, Scalar>::KdTree

Definition at line 100 of file gicp.h.

◆ InputKdTreePtr

template<typename PointSource , typename PointTarget , typename Scalar = float>
using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::InputKdTreePtr = typename Registration<PointSource, PointTarget, Scalar>::KdTreePtr

Definition at line 101 of file gicp.h.

◆ MatricesVector

template<typename PointSource , typename PointTarget , typename Scalar = float>
using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::MatricesVector = std::vector<Eigen::Matrix3d, Eigen::aligned_allocator<Eigen::Matrix3d> >

Definition at line 95 of file gicp.h.

◆ MatricesVectorConstPtr

template<typename PointSource , typename PointTarget , typename Scalar = float>
using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::MatricesVectorConstPtr = shared_ptr<const MatricesVector>

Definition at line 98 of file gicp.h.

◆ MatricesVectorPtr

template<typename PointSource , typename PointTarget , typename Scalar = float>
using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::MatricesVectorPtr = shared_ptr<MatricesVector>

Definition at line 97 of file gicp.h.

◆ Matrix3

template<typename PointSource , typename PointTarget , typename Scalar = float>
using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::Matrix3 = typename Eigen::Matrix<Scalar, 3, 3>

Definition at line 112 of file gicp.h.

◆ Matrix4

template<typename PointSource , typename PointTarget , typename Scalar = float>
using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::Matrix4 = typename IterativeClosestPoint<PointSource, PointTarget, Scalar>::Matrix4

Definition at line 113 of file gicp.h.

◆ Matrix6d

template<typename PointSource , typename PointTarget , typename Scalar = float>
using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::Matrix6d = Eigen::Matrix<double, 6, 6>

Definition at line 115 of file gicp.h.

◆ PointCloudSource

template<typename PointSource , typename PointTarget , typename Scalar = float>
using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::PointCloudSource = pcl::PointCloud<PointSource>

Definition at line 84 of file gicp.h.

◆ PointCloudSourceConstPtr

template<typename PointSource , typename PointTarget , typename Scalar = float>
using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::PointCloudSourceConstPtr = typename PointCloudSource::ConstPtr

Definition at line 86 of file gicp.h.

◆ PointCloudSourcePtr

template<typename PointSource , typename PointTarget , typename Scalar = float>
using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::PointCloudSourcePtr = typename PointCloudSource::Ptr

Definition at line 85 of file gicp.h.

◆ PointCloudTarget

template<typename PointSource , typename PointTarget , typename Scalar = float>
using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::PointCloudTarget = pcl::PointCloud<PointTarget>

Definition at line 88 of file gicp.h.

◆ PointCloudTargetConstPtr

template<typename PointSource , typename PointTarget , typename Scalar = float>
using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::PointCloudTargetConstPtr = typename PointCloudTarget::ConstPtr

Definition at line 90 of file gicp.h.

◆ PointCloudTargetPtr

template<typename PointSource , typename PointTarget , typename Scalar = float>
using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::PointCloudTargetPtr = typename PointCloudTarget::Ptr

Definition at line 89 of file gicp.h.

◆ PointIndicesConstPtr

template<typename PointSource , typename PointTarget , typename Scalar = float>
using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::PointIndicesConstPtr = PointIndices::ConstPtr

Definition at line 93 of file gicp.h.

◆ PointIndicesPtr

template<typename PointSource , typename PointTarget , typename Scalar = float>
using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::PointIndicesPtr = PointIndices::Ptr

Definition at line 92 of file gicp.h.

◆ Ptr

template<typename PointSource , typename PointTarget , typename Scalar = float>
using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::Ptr = shared_ptr<GeneralizedIterativeClosestPoint<PointSource, PointTarget, Scalar> >

Definition at line 104 of file gicp.h.

◆ Vector3

template<typename PointSource , typename PointTarget , typename Scalar = float>
using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::Vector3 = typename Eigen::Matrix<Scalar, 3, 1>

Definition at line 109 of file gicp.h.

◆ Vector4

template<typename PointSource , typename PointTarget , typename Scalar = float>
using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::Vector4 = typename Eigen::Matrix<Scalar, 4, 1>

Definition at line 110 of file gicp.h.

◆ Vector6d

template<typename PointSource , typename PointTarget , typename Scalar = float>
using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::Vector6d = Eigen::Matrix<double, 6, 1>

Definition at line 111 of file gicp.h.

Constructor & Destructor Documentation

◆ GeneralizedIterativeClosestPoint()

template<typename PointSource , typename PointTarget , typename Scalar = float>
PCL_MAKE_ALIGNED_OPERATOR_NEW pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::GeneralizedIterativeClosestPoint ( )
inline

Member Function Documentation

◆ applyState()

template<typename PointSource , typename PointTarget , typename Scalar >
void pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::applyState ( Matrix4 t,
const Vector6d x 
) const
protected

compute transformation matrix from transformation matrix

Definition at line 903 of file gicp.hpp.

◆ computeCovariances()

template<typename PointSource , typename PointTarget , typename Scalar >
template<typename PointT >
void pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::computeCovariances ( typename pcl::PointCloud< PointT >::ConstPtr  cloud,
const typename pcl::search::KdTree< PointT >::Ptr  tree,
MatricesVector cloud_covariances 
)
protected

compute points covariances matrices according to the K nearest neighbors.

K is set via setCorrespondenceRandomness() method.

Parameters
[in]cloudpointer to point cloud
[in]treeKD tree performer for nearest neighbors search
[out]cloud_covariancescovariances matrices for each point in the cloud

Definition at line 51 of file gicp.hpp.

References pcl::PointCloud< PointT >::begin(), pcl::PointCloud< PointT >::end(), pcl::search::KdTree< PointT, Tree >::nearestKSearch(), and pcl::PointCloud< PointT >::size().

◆ computeRDerivative()

template<typename PointSource , typename PointTarget , typename Scalar >
void pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::computeRDerivative ( const Vector6d x,
const Eigen::Matrix3d &  dCost_dR_T,
Vector6d g 
) const

Computes the derivative of the cost function w.r.t rotation angles.

rotation matrix is obtainded from rotation angles x[3], x[4] and x[5]

Returns
d/d_Phi, d/d_Theta, d/d_Psi respectively in g[3], g[4] and g[5]
Parameters
[in]xarray representing 3D transformation
[in]dCost_dR_Tthe transpose of the derivative of the cost function w.r.t rotation matrix
[out]ggradient vector

Definition at line 182 of file gicp.hpp.

◆ computeTransformation()

template<typename PointSource , typename PointTarget , typename Scalar >
void pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::computeTransformation ( PointCloudSource output,
const Matrix4 guess 
)
inlineoverrideprotected

Rigid transformation computation method with initial guess.

Parameters
outputthe transformed input point cloud dataset using the rigid transformation found
guessthe initial guess of the transformation to compute

Definition at line 746 of file gicp.hpp.

References pcl::Registration< PointSource, PointTarget, float >::initComputeReciprocal(), and pcl::transformPointCloud().

◆ estimateRigidTransformationBFGS()

template<typename PointSource , typename PointTarget , typename Scalar >
void pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::estimateRigidTransformationBFGS ( const PointCloudSource cloud_src,
const pcl::Indices indices_src,
const PointCloudTarget cloud_tgt,
const pcl::Indices indices_tgt,
Matrix4 transformation_matrix 
)

Estimate a rigid rotation transformation between a source and a target point cloud using an iterative non-linear BFGS approach.

Parameters
[in]cloud_srcthe source point cloud dataset
[in]indices_srcthe vector of indices describing the points of interest in cloud_src
[in]cloud_tgtthe target point cloud dataset
[in]indices_tgtthe vector of indices describing the correspondences of the interest points from indices_src
[in,out]transformation_matrixthe resultant transformation matrix

Definition at line 273 of file gicp.hpp.

References BFGS< FunctorType >::minimizeInit(), BFGS< FunctorType >::minimizeOneStep(), BFGSSpace::NoProgress, BFGS< FunctorType >::Parameters::order, BFGS< FunctorType >::parameters, BFGS< FunctorType >::Parameters::rho, BFGSSpace::Running, BFGS< FunctorType >::Parameters::sigma, BFGSSpace::Success, BFGS< FunctorType >::Parameters::tau1, BFGS< FunctorType >::Parameters::tau2, BFGS< FunctorType >::Parameters::tau3, and BFGS< FunctorType >::testGradient().

Referenced by pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::useBFGS().

◆ estimateRigidTransformationNewton()

template<typename PointSource , typename PointTarget , typename Scalar >
void pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::estimateRigidTransformationNewton ( const PointCloudSource cloud_src,
const pcl::Indices indices_src,
const PointCloudTarget cloud_tgt,
const pcl::Indices indices_tgt,
Matrix4 transformation_matrix 
)

Estimate a rigid rotation transformation between a source and a target point cloud using an iterative non-linear Newton approach.

Parameters
[in]cloud_srcthe source point cloud dataset
[in]indices_srcthe vector of indices describing the points of interest in cloud_src
[in]cloud_tgtthe target point cloud dataset
[in]indices_tgtthe vector of indices describing the correspondences of the interest points from indices_src
[in,out]transformation_matrixthe resultant transformation matrix

Definition at line 349 of file gicp.hpp.

References pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::OptimizationFunctorWithIndices::dfddf().

Referenced by pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::GeneralizedIterativeClosestPoint().

◆ getCorrespondenceRandomness()

template<typename PointSource , typename PointTarget , typename Scalar = float>
int pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::getCorrespondenceRandomness ( ) const
inline

Get the number of neighbors used when computing covariances as set by the user.

Definition at line 286 of file gicp.h.

References pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::k_correspondences_.

◆ getMaximumOptimizerIterations()

template<typename PointSource , typename PointTarget , typename Scalar = float>
int pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::getMaximumOptimizerIterations ( ) const
inline

Return maximum number of iterations at the optimization step.

Definition at line 318 of file gicp.h.

References pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::max_inner_iterations_.

◆ getRotationEpsilon()

template<typename PointSource , typename PointTarget , typename Scalar = float>
double pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::getRotationEpsilon ( ) const
inline

Get the rotation epsilon (maximum allowable difference between two consecutive rotations) as set by the user.

Definition at line 265 of file gicp.h.

References pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::rotation_epsilon_.

◆ getRotationGradientTolerance()

template<typename PointSource , typename PointTarget , typename Scalar = float>
double pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::getRotationGradientTolerance ( ) const
inline

Return the minimal rotation gradient threshold for early optimization stop.

Definition at line 353 of file gicp.h.

References pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::rotation_gradient_tolerance_.

◆ getTranslationGradientTolerance()

template<typename PointSource , typename PointTarget , typename Scalar = float>
double pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::getTranslationGradientTolerance ( ) const
inline

Return the minimal translation gradient threshold for early optimization stop.

Definition at line 336 of file gicp.h.

References pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::translation_gradient_tolerance_.

◆ mahalanobis()

template<typename PointSource , typename PointTarget , typename Scalar = float>
const Eigen::Matrix3d& pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::mahalanobis ( std::size_t  index) const
inline
Returns
Mahalanobis distance matrix for the given point index

Definition at line 231 of file gicp.h.

References pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::mahalanobis_.

◆ matricesInnerProd()

template<typename PointSource , typename PointTarget , typename Scalar = float>
double pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::matricesInnerProd ( const Eigen::MatrixXd &  mat1,
const Eigen::MatrixXd &  mat2 
) const
inlineprotected
Returns
trace of mat1 . mat2
Parameters
mat1matrix of dimension nxm
mat2matrix of dimension mxp

Definition at line 426 of file gicp.h.

◆ searchForNeighbors()

template<typename PointSource , typename PointTarget , typename Scalar = float>
bool pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::searchForNeighbors ( const PointSource &  query,
pcl::Indices index,
std::vector< float > &  distance 
)
inlineprotected

Search for the closest nearest neighbor of a given point.

Parameters
querythe point to search a nearest neighbour for
indexvector of size 1 to store the index of the nearest neighbour found
distancevector of size 1 to store the distance to nearest neighbour found

Definition at line 452 of file gicp.h.

References pcl::geometry::distance(), and pcl::Registration< PointSource, PointTarget, float >::tree_.

◆ setCorrespondenceRandomness()

template<typename PointSource , typename PointTarget , typename Scalar = float>
void pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::setCorrespondenceRandomness ( int  k)
inline

Set the number of neighbors used when selecting a point neighbourhood to compute covariances.

A higher value will bring more accurate covariance matrix but will make covariances computation slower.

Parameters
kthe number of neighbors to use when computing covariances

Definition at line 277 of file gicp.h.

References pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::k_correspondences_.

◆ setInputSource()

template<typename PointSource , typename PointTarget , typename Scalar = float>
void pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::setInputSource ( const PointCloudSourceConstPtr cloud)
inlineoverride

◆ setInputTarget()

template<typename PointSource , typename PointTarget , typename Scalar = float>
void pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::setInputTarget ( const PointCloudTargetConstPtr target)
inlineoverride

Provide a pointer to the input target (e.g., the point cloud that we want to align the input source to)

Parameters
[in]targetthe input point cloud target

Definition at line 176 of file gicp.h.

References pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::setInputTarget(), and pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::target_covariances_.

◆ setMaximumOptimizerIterations()

template<typename PointSource , typename PointTarget , typename Scalar = float>
void pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::setMaximumOptimizerIterations ( int  max)
inline

Set maximum number of iterations at the optimization step.

Parameters
[in]maxmaximum number of iterations for the optimizer

Definition at line 310 of file gicp.h.

References pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::max_inner_iterations_.

◆ setRotationEpsilon()

template<typename PointSource , typename PointTarget , typename Scalar = float>
void pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::setRotationEpsilon ( double  epsilon)
inline

Set the rotation epsilon (maximum allowable difference between two consecutive rotations) in order for an optimization to be considered as having converged to the final solution.

Parameters
epsilonthe rotation epsilon

Definition at line 256 of file gicp.h.

References pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::rotation_epsilon_.

◆ setRotationGradientTolerance()

template<typename PointSource , typename PointTarget , typename Scalar = float>
void pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::setRotationGradientTolerance ( double  tolerance)
inline

Set the minimal rotation gradient threshold for early optimization stop.

Parameters
[in]tolerancerotation gradient threshold in radians

Definition at line 345 of file gicp.h.

References pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::rotation_gradient_tolerance_.

◆ setSourceCovariances()

template<typename PointSource , typename PointTarget , typename Scalar = float>
void pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::setSourceCovariances ( const MatricesVectorPtr covariances)
inline

Provide a pointer to the covariances of the input source (if computed externally!).

If not set, GeneralizedIterativeClosestPoint will compute the covariances itself. Make sure to set the covariances AFTER setting the input source point cloud (setting the input source point cloud will reset the covariances).

Parameters
[in]covariancesthe input source covariances

Definition at line 167 of file gicp.h.

References pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::input_covariances_.

◆ setTargetCovariances()

template<typename PointSource , typename PointTarget , typename Scalar = float>
void pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::setTargetCovariances ( const MatricesVectorPtr covariances)
inline

Provide a pointer to the covariances of the input target (if computed externally!).

If not set, GeneralizedIterativeClosestPoint will compute the covariances itself. Make sure to set the covariances AFTER setting the input source point cloud (setting the input source point cloud will reset the covariances).

Parameters
[in]covariancesthe input target covariances

Definition at line 190 of file gicp.h.

References pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::target_covariances_.

◆ setTranslationGradientTolerance()

template<typename PointSource , typename PointTarget , typename Scalar = float>
void pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::setTranslationGradientTolerance ( double  tolerance)
inline

Set the minimal translation gradient threshold for early optimization stop.

Parameters
[in]tolerancetranslation gradient threshold in meters

Definition at line 327 of file gicp.h.

References pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::translation_gradient_tolerance_.

◆ useBFGS()

template<typename PointSource , typename PointTarget , typename Scalar = float>
void pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::useBFGS ( )
inline

Member Data Documentation

◆ base_transformation_

template<typename PointSource , typename PointTarget , typename Scalar = float>
Matrix4 pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::base_transformation_
protected

base transformation

Definition at line 377 of file gicp.h.

◆ gicp_epsilon_

template<typename PointSource , typename PointTarget , typename Scalar = float>
double pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::gicp_epsilon_ {0.001}
protected

The epsilon constant for gicp paper; this is NOT the convergence tolerance default: 0.001.

Definition at line 368 of file gicp.h.

◆ input_covariances_

template<typename PointSource , typename PointTarget , typename Scalar = float>
MatricesVectorPtr pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::input_covariances_
protected

◆ k_correspondences_

template<typename PointSource , typename PointTarget , typename Scalar = float>
int pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::k_correspondences_ {20}
protected

◆ mahalanobis_

template<typename PointSource , typename PointTarget , typename Scalar = float>
std::vector<Eigen::Matrix3d> pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::mahalanobis_
protected

Mahalanobis matrices holder.

Definition at line 398 of file gicp.h.

Referenced by pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::mahalanobis().

◆ max_inner_iterations_

template<typename PointSource , typename PointTarget , typename Scalar = float>
int pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::max_inner_iterations_ {20}
protected

◆ rigid_transformation_estimation_

template<typename PointSource , typename PointTarget , typename Scalar = float>
std::function<void(const pcl::PointCloud<PointSource>& cloud_src, const pcl::Indices& src_indices, const pcl::PointCloud<PointTarget>& cloud_tgt, const pcl::Indices& tgt_indices, Matrix4& transformation_matrix)> pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::rigid_transformation_estimation_
protected

◆ rotation_epsilon_

template<typename PointSource , typename PointTarget , typename Scalar = float>
double pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::rotation_epsilon_ {2e-3}
protected

The epsilon constant for rotation error.

(In GICP the transformation epsilon is split in rotation part and translation part). default: 2e-3

Definition at line 374 of file gicp.h.

Referenced by pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::getRotationEpsilon(), and pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::setRotationEpsilon().

◆ rotation_gradient_tolerance_

template<typename PointSource , typename PointTarget , typename Scalar = float>
double pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::rotation_gradient_tolerance_ {1e-2}
protected

◆ target_covariances_

template<typename PointSource , typename PointTarget , typename Scalar = float>
MatricesVectorPtr pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::target_covariances_
protected

◆ tmp_idx_src_

template<typename PointSource , typename PointTarget , typename Scalar = float>
const pcl::Indices* pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::tmp_idx_src_
protected

Temporary pointer to the source dataset indices.

Definition at line 386 of file gicp.h.

◆ tmp_idx_tgt_

template<typename PointSource , typename PointTarget , typename Scalar = float>
const pcl::Indices* pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::tmp_idx_tgt_
protected

Temporary pointer to the target dataset indices.

Definition at line 389 of file gicp.h.

◆ tmp_src_

template<typename PointSource , typename PointTarget , typename Scalar = float>
const PointCloudSource* pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::tmp_src_
protected

Temporary pointer to the source dataset.

Definition at line 380 of file gicp.h.

◆ tmp_tgt_

template<typename PointSource , typename PointTarget , typename Scalar = float>
const PointCloudTarget* pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::tmp_tgt_
protected

Temporary pointer to the target dataset.

Definition at line 383 of file gicp.h.

◆ translation_gradient_tolerance_

template<typename PointSource , typename PointTarget , typename Scalar = float>
double pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::translation_gradient_tolerance_ {1e-2}
protected

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