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>
Classes |
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| struct | OptimizationFunctorWithIndices |
| optimization functor structure More... |
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Public Member Functions |
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| GeneralizedIterativeClosestPoint () | |
| Empty constructor. More... |
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| void | setInputSource (const PointCloudSourceConstPtr &cloud) override |
| Provide a pointer to the input dataset. More... |
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| void | setSourceCovariances (const MatricesVectorPtr &covariances) |
| Provide a pointer to the covariances of the input source (if computed externally!). More... |
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| 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... |
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| void | setTargetCovariances (const MatricesVectorPtr &covariances) |
| Provide a pointer to the covariances of the input target (if computed externally!). More... |
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| void | estimateRigidTransformationBFGS (const PointCloudSource &cloud_src, const pcl::Indices &indices_src, const PointCloudTarget &cloud_tgt, const pcl::Indices &indices_tgt, Eigen::Matrix4f &transformation_matrix) |
| Estimate a rigid rotation transformation between a source and a target point cloud using an iterative non-linear Levenberg-Marquardt approach. More... |
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| const Eigen::Matrix3d & | mahalanobis (std::size_t index) const |
| void | computeRDerivative (const Vector6d &x, const Eigen::Matrix3d &R, Vector6d &g) const |
| Computes rotation matrix derivative. More... |
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| 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... |
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| double | getRotationEpsilon () const |
| Get the rotation epsilon (maximum allowable difference between two consecutive rotations) as set by the user. More... |
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| void | setCorrespondenceRandomness (int k) |
| Set the number of neighbors used when selecting a point neighbourhood to compute covariances. More... |
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| int | getCorrespondenceRandomness () const |
| Get the number of neighbors used when computing covariances as set by the user. More... |
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| void | setMaximumOptimizerIterations (int max) |
| Set maximum number of iterations at the optimization step. More... |
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| int | getMaximumOptimizerIterations () const |
| Return maximum number of iterations at the optimization step. More... |
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| void | setTranslationGradientTolerance (double tolerance) |
| Set the minimal translation gradient threshold for early optimization stop. More... |
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| double | getTranslationGradientTolerance () const |
| Return the minimal translation gradient threshold for early optimization stop. More... |
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| void | setRotationGradientTolerance (double tolerance) |
| Set the minimal rotation gradient threshold for early optimization stop. More... |
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| double | getRotationGradientTolerance () const |
| Return the minimal rotation gradient threshold for early optimization stop. More... |
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 Public Member Functions inherited from pcl::IterativeClosestPoint< PointSource, PointTarget > |
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| IterativeClosestPoint () | |
| Empty constructor. More... |
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| 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... |
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| IterativeClosestPoint (IterativeClosestPoint &&)=delete | |
| IterativeClosestPoint & | operator= (const IterativeClosestPoint &)=delete |
| IterativeClosestPoint & | operator= (IterativeClosestPoint &&)=delete |
| ~IterativeClosestPoint () | |
| Empty destructor. More... |
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| pcl::registration::DefaultConvergenceCriteria< float >::Ptr | getConvergeCriteria () |
| Returns a pointer to the DefaultConvergenceCriteria used by the IterativeClosestPoint class. More... |
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| 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... |
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| void | setInputTarget (const PointCloudTargetConstPtr &cloud) override |
| Provide a pointer to the input target (e.g., the point cloud that we want to align to the target) More... |
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| void | setUseReciprocalCorrespondences (bool use_reciprocal_correspondence) |
| Set whether to use reciprocal correspondence or not. More... |
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| bool | getUseReciprocalCorrespondences () const |
| Obtain whether reciprocal correspondence are used or not. More... |
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| Public Member Functions inherited from pcl::Registration< PointSource, PointTarget, float > |
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| Registration () | |
| Empty constructor. More... |
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| ~Registration () | |
| destructor. More... |
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| void | setTransformationEstimation (const TransformationEstimationPtr &te) |
| Provide a pointer to the transformation estimation object. More... |
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| void | setCorrespondenceEstimation (const CorrespondenceEstimationPtr &ce) |
| Provide a pointer to the correspondence estimation object. More... |
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| const PointCloudSourceConstPtr | getInputSource () |
| Get a pointer to the input point cloud dataset target. More... |
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| const PointCloudTargetConstPtr | getInputTarget () |
| Get a pointer to the input point cloud dataset target. More... |
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| 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... |
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| KdTreePtr | getSearchMethodTarget () const |
| Get a pointer to the search method used to find correspondences in the target cloud. More... |
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| 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... |
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| KdTreeReciprocalPtr | getSearchMethodSource () const |
| Get a pointer to the search method used to find correspondences in the source cloud. More... |
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| Matrix4 | getFinalTransformation () |
| Get the final transformation matrix estimated by the registration method. More... |
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| Matrix4 | getLastIncrementalTransformation () |
| Get the last incremental transformation matrix estimated by the registration method. More... |
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| void | setMaximumIterations (int nr_iterations) |
| Set the maximum number of iterations the internal optimization should run for. More... |
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| int | getMaximumIterations () |
| Get the maximum number of iterations the internal optimization should run for, as set by the user. More... |
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| void | setRANSACIterations (int ransac_iterations) |
| Set the number of iterations RANSAC should run for. More... |
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| double | getRANSACIterations () |
| Get the number of iterations RANSAC should run for, as set by the user. More... |
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| void | setRANSACOutlierRejectionThreshold (double inlier_threshold) |
| Set the inlier distance threshold for the internal RANSAC outlier rejection loop. More... |
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| double | getRANSACOutlierRejectionThreshold () |
| Get the inlier distance threshold for the internal outlier rejection loop as set by the user. More... |
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| void | setMaxCorrespondenceDistance (double distance_threshold) |
| Set the maximum distance threshold between two correspondent points in source <-> target. More... |
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| double | getMaxCorrespondenceDistance () |
| Get the maximum distance threshold between two correspondent points in source <-> target. More... |
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| 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... |
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| double | getTransformationEpsilon () |
| Get the transformation epsilon (maximum allowable translation squared difference between two consecutive transformations) as set by the user. More... |
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| 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... |
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| 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... |
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| 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... |
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| double | getEuclideanFitnessEpsilon () |
| Get the maximum allowed distance error before the algorithm will be considered to have converged, as set by the user. More... |
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| void | setPointRepresentation (const PointRepresentationConstPtr &point_representation) |
| Provide a boost shared pointer to the PointRepresentation to be used when comparing points. More... |
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| 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... |
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| 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... |
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| 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... |
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| bool | hasConverged () const |
| Return the state of convergence after the last align run. More... |
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| void | align (PointCloudSource &output) |
| Call the registration algorithm which estimates the transformation and returns the transformed source (input) as output. More... |
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| void | align (PointCloudSource &output, const Matrix4 &guess) |
| Call the registration algorithm which estimates the transformation and returns the transformed source (input) as output. More... |
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| const std::string & | getClassName () const |
| Abstract class get name method. More... |
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| bool | initCompute () |
| Internal computation initialization. More... |
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| bool | initComputeReciprocal () |
| Internal computation when reciprocal lookup is needed. More... |
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| void | addCorrespondenceRejector (const CorrespondenceRejectorPtr &rejector) |
| Add a new correspondence rejector to the list. More... |
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| std::vector< CorrespondenceRejectorPtr > | getCorrespondenceRejectors () |
| Get the list of correspondence rejectors. More... |
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| bool | removeCorrespondenceRejector (unsigned int i) |
| Remove the i-th correspondence rejector in the list. More... |
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| void | clearCorrespondenceRejectors () |
| Clear the list of correspondence rejectors. More... |
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| Public Member Functions inherited from pcl::PCLBase< PointSource > |
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| PCLBase () | |
| Empty constructor. More... |
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| PCLBase (const PCLBase &base) | |
| Copy constructor. More... |
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| virtual | ~PCLBase ()=default |
| Destructor. More... |
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| virtual void | setInputCloud (const PointCloudConstPtr &cloud) |
| Provide a pointer to the input dataset. More... |
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| const PointCloudConstPtr | getInputCloud () const |
| Get a pointer to the input point cloud dataset. More... |
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| virtual void | setIndices (const IndicesPtr &indices) |
| Provide a pointer to the vector of indices that represents the input data. More... |
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| virtual void | setIndices (const IndicesConstPtr &indices) |
| Provide a pointer to the vector of indices that represents the input data. More... |
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| virtual void | setIndices (const PointIndicesConstPtr &indices) |
| Provide a pointer to the vector of indices that represents the input data. More... |
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| 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... |
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| IndicesPtr | getIndices () |
| Get a pointer to the vector of indices used. More... |
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| const IndicesConstPtr | getIndices () const |
| Get a pointer to the vector of indices used. More... |
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| const PointSource & | operator[] (std::size_t pos) const |
| Override PointCloud operator[] to shorten code. More... |
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Protected Member Functions |
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| 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... |
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| double | matricesInnerProd (const Eigen::MatrixXd &mat1, const Eigen::MatrixXd &mat2) const |
| void | computeTransformation (PointCloudSource &output, const Eigen::Matrix4f &guess) override |
| Rigid transformation computation method with initial guess. More... |
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| bool | searchForNeighbors (const PointSource &query, pcl::Indices &index, std::vector< float > &distance) |
| Search for the closest nearest neighbor of a given point. More... |
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| void | applyState (Eigen::Matrix4f &t, const Vector6d &x) const |
| compute transformation matrix from transformation matrix More... |
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| Protected Member Functions inherited from pcl::IterativeClosestPoint< PointSource, PointTarget > |
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| virtual void | transformCloud (const PointCloudSource &input, PointCloudSource &output, const Matrix4 &transform) |
| Apply a rigid transform to a given dataset. More... |
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| void | computeTransformation (PointCloudSource &output, const Matrix4 &guess) override |
| Rigid transformation computation method with initial guess. More... |
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| virtual void | determineRequiredBlobData () |
| Looks at the Estimators and Rejectors and determines whether their blob-setter methods need to be called. More... |
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| Protected Member Functions inherited from pcl::Registration< PointSource, PointTarget, float > |
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| 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... |
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| virtual void | computeTransformation (PointCloudSource &output, const Matrix4 &guess)=0 |
| Abstract transformation computation method with initial guess. More... |
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| Protected Member Functions inherited from pcl::PCLBase< PointSource > |
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| bool | initCompute () |
| This method should get called before starting the actual computation. More... |
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| bool | deinitCompute () |
| This method should get called after finishing the actual computation. More... |
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Protected Attributes |
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| int | k_correspondences_ |
| The number of neighbors used for covariances computation. More... |
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| double | gicp_epsilon_ |
| The epsilon constant for gicp paper; this is NOT the convergence tolerance default: 0.001. More... |
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| double | rotation_epsilon_ |
| The epsilon constant for rotation error. More... |
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| Eigen::Matrix4f | base_transformation_ |
| base transformation More... |
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| const PointCloudSource * | tmp_src_ |
| Temporary pointer to the source dataset. More... |
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| const PointCloudTarget * | tmp_tgt_ |
| Temporary pointer to the target dataset. More... |
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| const pcl::Indices * | tmp_idx_src_ |
| Temporary pointer to the source dataset indices. More... |
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| const pcl::Indices * | tmp_idx_tgt_ |
| Temporary pointer to the target dataset indices. More... |
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| MatricesVectorPtr | input_covariances_ |
| Input cloud points covariances. More... |
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| MatricesVectorPtr | target_covariances_ |
| Target cloud points covariances. More... |
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| std::vector< Eigen::Matrix3d > | mahalanobis_ |
| Mahalanobis matrices holder. More... |
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| int | max_inner_iterations_ |
| maximum number of optimizations More... |
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| double | translation_gradient_tolerance_ |
| minimal translation gradient for early optimization stop More... |
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| double | rotation_gradient_tolerance_ |
| minimal rotation gradient for early optimization stop More... |
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| 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, Eigen::Matrix4f &transformation_matrix)> | rigid_transformation_estimation_ |
| Protected Attributes inherited from pcl::IterativeClosestPoint< PointSource, PointTarget > |
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| std::size_t | x_idx_offset_ |
| XYZ fields offset. More... |
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| std::size_t | y_idx_offset_ |
| std::size_t | z_idx_offset_ |
| std::size_t | nx_idx_offset_ |
| Normal fields offset. More... |
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| std::size_t | ny_idx_offset_ |
| std::size_t | nz_idx_offset_ |
| bool | use_reciprocal_correspondence_ |
| The correspondence type used for correspondence estimation. More... |
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| bool | source_has_normals_ |
| Internal check whether source dataset has normals or not. More... |
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| bool | target_has_normals_ |
| Internal check whether target dataset has normals or not. More... |
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| bool | need_source_blob_ |
| Checks for whether estimators and rejectors need various data. More... |
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| bool | need_target_blob_ |
| Protected Attributes inherited from pcl::Registration< PointSource, PointTarget, float > |
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| std::string | reg_name_ |
| The registration method name. More... |
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| KdTreePtr | tree_ |
| A pointer to the spatial search object. More... |
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| KdTreeReciprocalPtr | tree_reciprocal_ |
| A pointer to the spatial search object of the source. More... |
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| int | nr_iterations_ |
| The number of iterations the internal optimization ran for (used internally). More... |
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| int | max_iterations_ |
| The maximum number of iterations the internal optimization should run for. More... |
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| int | ransac_iterations_ |
| The number of iterations RANSAC should run for. More... |
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| PointCloudTargetConstPtr | target_ |
| The input point cloud dataset target. More... |
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| Matrix4 | final_transformation_ |
| The final transformation matrix estimated by the registration method after N iterations. More... |
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| Matrix4 | transformation_ |
| The transformation matrix estimated by the registration method. More... |
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| Matrix4 | previous_transformation_ |
| The previous transformation matrix estimated by the registration method (used internally). More... |
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| double | transformation_epsilon_ |
| The maximum difference between two consecutive transformations in order to consider convergence (user defined). More... |
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| double | transformation_rotation_epsilon_ |
| The maximum rotation difference between two consecutive transformations in order to consider convergence (user defined). More... |
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| 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... |
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| double | corr_dist_threshold_ |
| The maximum distance threshold between two correspondent points in source <-> target. More... |
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| double | inlier_threshold_ |
| The inlier distance threshold for the internal RANSAC outlier rejection loop. More... |
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| bool | converged_ |
| Holds internal convergence state, given user parameters. More... |
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| int | min_number_correspondences_ |
| The minimum number of correspondences that the algorithm needs before attempting to estimate the transformation. More... |
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| CorrespondencesPtr | correspondences_ |
| The set of correspondences determined at this ICP step. More... |
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| TransformationEstimationPtr | transformation_estimation_ |
| A TransformationEstimation object, used to calculate the 4x4 rigid transformation. More... |
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| CorrespondenceEstimationPtr | correspondence_estimation_ |
| A CorrespondenceEstimation object, used to estimate correspondences between the source and the target cloud. More... |
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| std::vector< CorrespondenceRejectorPtr > | correspondence_rejectors_ |
| The list of correspondence rejectors to use. More... |
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| bool | target_cloud_updated_ |
| Variable that stores whether we have a new target cloud, meaning we need to pre-process it again. More... |
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| bool | source_cloud_updated_ |
| Variable that stores whether we have a new source cloud, meaning we need to pre-process it again. More... |
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| bool | force_no_recompute_ |
| A flag which, if set, means the tree operating on the target cloud will never be recomputed. More... |
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| bool | force_no_recompute_reciprocal_ |
| A flag which, if set, means the tree operating on the source cloud will never be recomputed. More... |
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| std::function< UpdateVisualizerCallbackSignature > | update_visualizer_ |
| Callback function to update intermediate source point cloud position during it's registration to the target point cloud. More... |
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| Protected Attributes inherited from pcl::PCLBase< PointSource > |
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| PointCloudConstPtr | input_ |
| The input point cloud dataset. More... |
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| IndicesPtr | indices_ |
| A pointer to the vector of point indices to use. More... |
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| bool | use_indices_ |
| Set to true if point indices are used. More... |
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| bool | fake_indices_ |
| If no set of indices are given, we construct a set of fake indices that mimic the input PointCloud. More... |
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Additional Inherited Members |
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| Public Attributes inherited from pcl::IterativeClosestPoint< PointSource, PointTarget > |
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| pcl::registration::DefaultConvergenceCriteria< float >::Ptr | convergence_criteria_ |
Detailed Description
template<typename PointSource, typename PointTarget>
class pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >
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 anistropic 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 an eigen mapped BFGS and FLANN.
Member Typedef Documentation
ConstPtr
template<typename PointSource , typename PointTarget >
| using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::ConstPtr = shared_ptr<const GeneralizedIterativeClosestPoint<PointSource, PointTarget> > |
InputKdTree
template<typename PointSource , typename PointTarget >
| using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::InputKdTree = typename Registration<PointSource, PointTarget>::KdTree |
InputKdTreePtr
template<typename PointSource , typename PointTarget >
| using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::InputKdTreePtr = typename Registration<PointSource, PointTarget>::KdTreePtr |
MatricesVector
template<typename PointSource , typename PointTarget >
| using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::MatricesVector = std::vector<Eigen::Matrix3d, Eigen::aligned_allocator<Eigen::Matrix3d> > |
MatricesVectorConstPtr
template<typename PointSource , typename PointTarget >
| using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::MatricesVectorConstPtr = shared_ptr<const MatricesVector> |
MatricesVectorPtr
template<typename PointSource , typename PointTarget >
| using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::MatricesVectorPtr = shared_ptr<MatricesVector> |
PointCloudSource
template<typename PointSource , typename PointTarget >
| using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::PointCloudSource = pcl::PointCloud<PointSource> |
PointCloudSourceConstPtr
template<typename PointSource , typename PointTarget >
| using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::PointCloudSourceConstPtr = typename PointCloudSource::ConstPtr |
PointCloudSourcePtr
template<typename PointSource , typename PointTarget >
| using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::PointCloudSourcePtr = typename PointCloudSource::Ptr |
PointCloudTarget
template<typename PointSource , typename PointTarget >
| using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::PointCloudTarget = pcl::PointCloud<PointTarget> |
PointCloudTargetConstPtr
template<typename PointSource , typename PointTarget >
| using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::PointCloudTargetConstPtr = typename PointCloudTarget::ConstPtr |
PointCloudTargetPtr
template<typename PointSource , typename PointTarget >
| using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::PointCloudTargetPtr = typename PointCloudTarget::Ptr |
PointIndicesConstPtr
template<typename PointSource , typename PointTarget >
| using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::PointIndicesConstPtr = PointIndices::ConstPtr |
PointIndicesPtr
template<typename PointSource , typename PointTarget >
| using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::PointIndicesPtr = PointIndices::Ptr |
Ptr
template<typename PointSource , typename PointTarget >
| using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::Ptr = shared_ptr<GeneralizedIterativeClosestPoint<PointSource, PointTarget> > |
Vector6d
template<typename PointSource , typename PointTarget >
| using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::Vector6d = Eigen::Matrix<double, 6, 1> |
Constructor & Destructor Documentation
GeneralizedIterativeClosestPoint()
template<typename PointSource , typename PointTarget >
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inline |
Member Function Documentation
applyState()
template<typename PointSource , typename PointTarget >
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protected |
computeCovariances()
template<typename PointSource , typename PointTarget >
template<typename PointT >
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protected |
compute points covariances matrices according to the K nearest neighbors.
K is set via setCorrespondenceRandomness() method.
- Parameters
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cloud pointer to point cloud tree KD tree performer for nearest neighbors search [out] cloud_covariances covariances matrices for each point in the cloud
computeRDerivative()
template<typename PointSource , typename PointTarget >
| void pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::computeRDerivative | ( | const Vector6d & | x, |
| const Eigen::Matrix3d & | R, | ||
| Vector6d & | g | ||
| ) | const |
Computes rotation matrix derivative.
rotation matrix is obtainded from rotation angles x[3], x[4] and x[5]
- Returns
- d/d_rx, d/d_ry and d/d_rz respectively in g[3], g[4] and g[5] param x array representing 3D transformation param R rotation matrix param g gradient vector
computeTransformation()
template<typename PointSource , typename PointTarget >
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inlineoverrideprotected |
estimateRigidTransformationBFGS()
template<typename PointSource , typename PointTarget >
| void pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::estimateRigidTransformationBFGS | ( | const PointCloudSource & | cloud_src, |
| const pcl::Indices & | indices_src, | ||
| const PointCloudTarget & | cloud_tgt, | ||
| const pcl::Indices & | indices_tgt, | ||
| Eigen::Matrix4f & | transformation_matrix | ||
| ) |
Estimate a rigid rotation transformation between a source and a target point cloud using an iterative non-linear Levenberg-Marquardt approach.
- Parameters
-
[in] cloud_src the source point cloud dataset [in] indices_src the vector of indices describing the points of interest in cloud_src [in] cloud_tgt the target point cloud dataset [in] indices_tgt the vector of indices describing the correspondences of the interest points from indices_src [out] transformation_matrix the resultant transformation matrix
Definition at line 188 of file gicp.hpp.
Referenced by pcl::GeneralizedIterativeClosestPoint< PointXYZRGBA, PointXYZRGBA >::GeneralizedIterativeClosestPoint().
getCorrespondenceRandomness()
template<typename PointSource , typename PointTarget >
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inline |
getMaximumOptimizerIterations()
template<typename PointSource , typename PointTarget >
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inline |
getRotationEpsilon()
template<typename PointSource , typename PointTarget >
|
inline |
getRotationGradientTolerance()
template<typename PointSource , typename PointTarget >
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inline |
getTranslationGradientTolerance()
template<typename PointSource , typename PointTarget >
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inline |
mahalanobis()
template<typename PointSource , typename PointTarget >
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inline |
matricesInnerProd()
template<typename PointSource , typename PointTarget >
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inlineprotected |
searchForNeighbors()
template<typename PointSource , typename PointTarget >
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inlineprotected |
Search for the closest nearest neighbor of a given point.
- Parameters
-
query the point to search a nearest neighbour for index vector of size 1 to store the index of the nearest neighbour found distance vector of size 1 to store the distance to nearest neighbour found
setCorrespondenceRandomness()
template<typename PointSource , typename PointTarget >
|
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
-
k the number of neighbors to use when computing covariances
setInputSource()
template<typename PointSource , typename PointTarget >
|
inlineoverridevirtual |
Provide a pointer to the input dataset.
- Parameters
-
cloud the const boost shared pointer to a PointCloud message
Reimplemented from pcl::Registration< PointSource, PointTarget, float >.
setInputTarget()
template<typename PointSource , typename PointTarget >
|
inlineoverridevirtual |
Provide a pointer to the input target (e.g., the point cloud that we want to align the input source to)
- Parameters
-
[in] target the input point cloud target
Reimplemented from pcl::Registration< PointSource, PointTarget, float >.
setMaximumOptimizerIterations()
template<typename PointSource , typename PointTarget >
|
inline |
setRotationEpsilon()
template<typename PointSource , typename PointTarget >
|
inline |
setRotationGradientTolerance()
template<typename PointSource , typename PointTarget >
|
inline |
setSourceCovariances()
template<typename PointSource , typename PointTarget >
|
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] covariances the input source covariances
setTargetCovariances()
template<typename PointSource , typename PointTarget >
|
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] covariances the input target covariances
setTranslationGradientTolerance()
template<typename PointSource , typename PointTarget >
|
inline |
Member Data Documentation
base_transformation_
template<typename PointSource , typename PointTarget >
|
protected |
gicp_epsilon_
template<typename PointSource , typename PointTarget >
|
protected |
input_covariances_
template<typename PointSource , typename PointTarget >
|
protected |
Input cloud points covariances.
Definition at line 347 of file gicp.h.
Referenced by pcl::GeneralizedIterativeClosestPoint< PointXYZRGBA, PointXYZRGBA >::setInputSource(), and pcl::GeneralizedIterativeClosestPoint< PointXYZRGBA, PointXYZRGBA >::setSourceCovariances().
k_correspondences_
template<typename PointSource , typename PointTarget >
|
protected |
The number of neighbors used for covariances computation.
default: 20
Definition at line 317 of file gicp.h.
Referenced by pcl::GeneralizedIterativeClosestPoint< PointXYZRGBA, PointXYZRGBA >::getCorrespondenceRandomness(), and pcl::GeneralizedIterativeClosestPoint< PointXYZRGBA, PointXYZRGBA >::setCorrespondenceRandomness().
mahalanobis_
template<typename PointSource , typename PointTarget >
|
protected |
Mahalanobis matrices holder.
Definition at line 353 of file gicp.h.
Referenced by pcl::GeneralizedIterativeClosestPoint< PointXYZRGBA, PointXYZRGBA >::mahalanobis().
max_inner_iterations_
template<typename PointSource , typename PointTarget >
|
protected |
maximum number of optimizations
Definition at line 356 of file gicp.h.
Referenced by pcl::GeneralizedIterativeClosestPoint< PointXYZRGBA, PointXYZRGBA >::getMaximumOptimizerIterations(), and pcl::GeneralizedIterativeClosestPoint< PointXYZRGBA, PointXYZRGBA >::setMaximumOptimizerIterations().
rigid_transformation_estimation_
template<typename PointSource , typename PointTarget >
|
protected |
Definition at line 443 of file gicp.h.
Referenced by pcl::GeneralizedIterativeClosestPoint< PointXYZRGBA, PointXYZRGBA >::GeneralizedIterativeClosestPoint().
rotation_epsilon_
template<typename PointSource , typename PointTarget >
|
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 329 of file gicp.h.
Referenced by pcl::GeneralizedIterativeClosestPoint< PointXYZRGBA, PointXYZRGBA >::getRotationEpsilon(), and pcl::GeneralizedIterativeClosestPoint< PointXYZRGBA, PointXYZRGBA >::setRotationEpsilon().
rotation_gradient_tolerance_
template<typename PointSource , typename PointTarget >
|
protected |
minimal rotation gradient for early optimization stop
Definition at line 362 of file gicp.h.
Referenced by pcl::GeneralizedIterativeClosestPoint< PointXYZRGBA, PointXYZRGBA >::getRotationGradientTolerance(), and pcl::GeneralizedIterativeClosestPoint< PointXYZRGBA, PointXYZRGBA >::setRotationGradientTolerance().
target_covariances_
template<typename PointSource , typename PointTarget >
|
protected |
Target cloud points covariances.
Definition at line 350 of file gicp.h.
Referenced by pcl::GeneralizedIterativeClosestPoint< PointXYZRGBA, PointXYZRGBA >::setInputTarget(), and pcl::GeneralizedIterativeClosestPoint< PointXYZRGBA, PointXYZRGBA >::setTargetCovariances().
tmp_idx_src_
template<typename PointSource , typename PointTarget >
|
protected |
tmp_idx_tgt_
template<typename PointSource , typename PointTarget >
|
protected |
tmp_src_
template<typename PointSource , typename PointTarget >
|
protected |
tmp_tgt_
template<typename PointSource , typename PointTarget >
|
protected |
translation_gradient_tolerance_
template<typename PointSource , typename PointTarget >
|
protected |
minimal translation gradient for early optimization stop
Definition at line 359 of file gicp.h.
Referenced by pcl::GeneralizedIterativeClosestPoint< PointXYZRGBA, PointXYZRGBA >::getTranslationGradientTolerance(), and pcl::GeneralizedIterativeClosestPoint< PointXYZRGBA, PointXYZRGBA >::setTranslationGradientTolerance().
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_generalized_iterative_closest_point.html
