Point Cloud Library (PCL)
1.11.1
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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 | |
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 >::KdTree |
using | InputKdTreePtr = typename Registration< PointSource, PointTarget >::KdTreePtr |
using | Ptr = shared_ptr< GeneralizedIterativeClosestPoint< PointSource, PointTarget > > |
using | ConstPtr = shared_ptr< const GeneralizedIterativeClosestPoint< PointSource, PointTarget > > |
using | Vector6d = Eigen::Matrix< double, 6, 1 > |
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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 |
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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 std::vector< int > &, const pcl::PointCloud< PointTarget > &, const std::vector< int > &) |
The callback signature to the function updating intermediate source point cloud position during it's registration to the target point cloud. More... | |
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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 | |
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 std::vector< int > &indices_src, const PointCloudTarget &cloud_tgt, const std::vector< int > &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... | |
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... | |
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 | 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... | |
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IterativeClosestPoint () | |
Empty constructor. More... | |
~IterativeClosestPoint () | |
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 to the target) 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... | |
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Registration () | |
Empty constructor. More... | |
~Registration () | |
destructor. More... | |
void | setTransformationEstimation (const TransformationEstimationPtr &te) |
Provide a pointer to the transformation estimation object. More... | |
void | setCorrespondenceEstimation (const CorrespondenceEstimationPtr &ce) |
Provide a pointer to the correspondence estimation object. More... | |
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< CorrespondenceRejectorPtr > | getCorrespondenceRejectors () |
Get the list of correspondence rejectors. More... | |
bool | removeCorrespondenceRejector (unsigned int i) |
Remove the i-th correspondence rejector in the list. More... | |
void | clearCorrespondenceRejectors () |
Clear the list of correspondence rejectors. More... | |
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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 Eigen::Matrix4f &guess) override |
Rigid transformation computation method with initial guess. More... | |
bool | searchForNeighbors (const PointSource &query, std::vector< int > &index, std::vector< float > &distance) |
Search for the closest nearest neighbor of a given point. More... | |
void | applyState (Eigen::Matrix4f &t, const Vector6d &x) const |
compute transformation matrix from transformation matrix More... | |
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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... | |
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bool | searchForNeighbors (const PointCloudSource &cloud, int index, std::vector< int > &indices, std::vector< float > &distances) |
Search for the closest nearest neighbor of a given point. More... | |
virtual void | computeTransformation (PointCloudSource &output, const Matrix4 &guess)=0 |
Abstract transformation computation method with initial guess. More... | |
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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_ |
The number of neighbors used for covariances computation. More... | |
double | gicp_epsilon_ |
The epsilon constant for gicp paper; this is NOT the convergence tolerance default: 0.001. More... | |
double | rotation_epsilon_ |
The epsilon constant for rotation error. More... | |
Eigen::Matrix4f | base_transformation_ |
base transformation More... | |
const PointCloudSource * | tmp_src_ |
Temporary pointer to the source dataset. More... | |
const PointCloudTarget * | tmp_tgt_ |
Temporary pointer to the target dataset. More... | |
const std::vector< int > * | tmp_idx_src_ |
Temporary pointer to the source dataset indices. More... | |
const std::vector< int > * | tmp_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_ |
maximum number of optimizations More... | |
double | translation_gradient_tolerance_ |
minimal translation gradient for early optimization stop More... | |
double | rotation_gradient_tolerance_ |
minimal rotation gradient for early optimization stop More... | |
std::function< void(const pcl::PointCloud< PointSource > &cloud_src, const std::vector< int > &src_indices, const pcl::PointCloud< PointTarget > &cloud_tgt, const std::vector< int > &tgt_indices, Eigen::Matrix4f &transformation_matrix)> | rigid_transformation_estimation_ |
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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_ |
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std::string | reg_name_ |
The registration method name. More... | |
KdTreePtr | tree_ |
A pointer to the spatial search object. More... | |
KdTreeReciprocalPtr | tree_reciprocal_ |
A pointer to the spatial search object of the source. More... | |
int | nr_iterations_ |
The number of iterations the internal optimization ran for (used internally). More... | |
int | max_iterations_ |
The maximum number of iterations the internal optimization should run for. More... | |
int | ransac_iterations_ |
The number of iterations RANSAC should run for. More... | |
PointCloudTargetConstPtr | target_ |
The input point cloud dataset target. More... | |
Matrix4 | final_transformation_ |
The final transformation matrix estimated by the registration method after N iterations. More... | |
Matrix4 | transformation_ |
The transformation matrix estimated by the registration method. More... | |
Matrix4 | previous_transformation_ |
The previous transformation matrix estimated by the registration method (used internally). More... | |
double | transformation_epsilon_ |
The maximum difference between two consecutive transformations in order to consider convergence (user defined). More... | |
double | transformation_rotation_epsilon_ |
The maximum rotation difference between two consecutive transformations in order to consider convergence (user defined). More... | |
double | euclidean_fitness_epsilon_ |
The maximum allowed Euclidean error between two consecutive steps in the ICP loop, before the algorithm is considered to have converged. More... | |
double | corr_dist_threshold_ |
The maximum distance threshold between two correspondent points in source <-> target. More... | |
double | inlier_threshold_ |
The inlier distance threshold for the internal RANSAC outlier rejection loop. More... | |
bool | converged_ |
Holds internal convergence state, given user parameters. More... | |
int | min_number_correspondences_ |
The minimum number of correspondences that the algorithm needs before attempting to estimate the transformation. More... | |
CorrespondencesPtr | correspondences_ |
The set of correspondences determined at this ICP step. More... | |
TransformationEstimationPtr | transformation_estimation_ |
A TransformationEstimation object, used to calculate the 4x4 rigid transformation. More... | |
CorrespondenceEstimationPtr | correspondence_estimation_ |
A CorrespondenceEstimation object, used to estimate correspondences between the source and the target cloud. More... | |
std::vector< CorrespondenceRejectorPtr > | correspondence_rejectors_ |
The list of correspondence rejectors to use. More... | |
bool | target_cloud_updated_ |
Variable that stores whether we have a new target cloud, meaning we need to pre-process it again. More... | |
bool | source_cloud_updated_ |
Variable that stores whether we have a new source cloud, meaning we need to pre-process it again. More... | |
bool | force_no_recompute_ |
A flag which, if set, means the tree operating on the target cloud will never be recomputed. More... | |
bool | force_no_recompute_reciprocal_ |
A flag which, if set, means the tree operating on the source cloud will never be recomputed. More... | |
std::function< UpdateVisualizerCallbackSignature > | update_visualizer_ |
Callback function to update intermediate source point cloud position during it's registration to the target point cloud. More... | |
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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 | |
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pcl::registration::DefaultConvergenceCriteria< float >::Ptr | convergence_criteria_ |
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.
using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::ConstPtr = shared_ptr< const GeneralizedIterativeClosestPoint<PointSource, PointTarget> > |
using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::InputKdTree = typename Registration<PointSource, PointTarget>::KdTree |
using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::InputKdTreePtr = typename Registration<PointSource, PointTarget>::KdTreePtr |
using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::MatricesVector = std::vector< Eigen::Matrix3d, Eigen::aligned_allocator<Eigen::Matrix3d> > |
using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::MatricesVectorConstPtr = shared_ptr<const MatricesVector> |
using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::MatricesVectorPtr = shared_ptr<MatricesVector> |
using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::PointCloudSource = pcl::PointCloud<PointSource> |
using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::PointCloudSourceConstPtr = typename PointCloudSource::ConstPtr |
using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::PointCloudSourcePtr = typename PointCloudSource::Ptr |
using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::PointCloudTarget = pcl::PointCloud<PointTarget> |
using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::PointCloudTargetConstPtr = typename PointCloudTarget::ConstPtr |
using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::PointCloudTargetPtr = typename PointCloudTarget::Ptr |
using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::PointIndicesConstPtr = PointIndices::ConstPtr |
using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::PointIndicesPtr = PointIndices::Ptr |
using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::Ptr = shared_ptr< GeneralizedIterativeClosestPoint<PointSource, PointTarget> > |
using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::Vector6d = Eigen::Matrix<double, 6, 1> |
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inline |
Empty constructor.
Definition at line 106 of file gicp.h.
References pcl::Registration< PointSource, PointTarget, float >::corr_dist_threshold_, pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::estimateRigidTransformationBFGS(), pcl::Registration< PointSource, PointTarget, float >::max_iterations_, pcl::Registration< PointSource, PointTarget, float >::min_number_correspondences_, pcl::Registration< PointSource, PointTarget, float >::reg_name_, pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::rigid_transformation_estimation_, and pcl::Registration< PointSource, PointTarget, float >::transformation_epsilon_.
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protected |
compute transformation matrix from transformation matrix
Definition at line 493 of file gicp.hpp.
Referenced by pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::OptimizationFunctorWithIndices::operator()().
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protected |
compute points covariances matrices according to the K nearest neighbors.
K is set via setCorrespondenceRandomness() method.
cloud | pointer to point cloud | |
tree | KD tree performer for nearest neighbors search | |
[out] | cloud_covariances | covariances matrices for each point in the cloud |
Definition at line 53 of file gicp.hpp.
References pcl::PointCloud< PointT >::begin(), pcl::PointCloud< PointT >::end(), pcl::search::KdTree< PointT, Tree >::nearestKSearch(), and pcl::PointCloud< PointT >::size().
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]
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inlineoverrideprotected |
Rigid transformation computation method with initial guess.
output | the transformed input point cloud dataset using the rigid transformation found |
guess | the initial guess of the transformation to compute |
Definition at line 370 of file gicp.hpp.
References pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::base_transformation_, pcl::Registration< PointSource, PointTarget, float >::converged_, pcl::Registration< PointSource, PointTarget, float >::corr_dist_threshold_, pcl::Registration< PointSource, PointTarget, float >::final_transformation_, pcl::Registration< PointSource, PointTarget, float >::getClassName(), pcl::PCLBase< PointSource >::indices_, pcl::Registration< PointSource, PointTarget, Scalar >::initComputeReciprocal(), pcl::PCLBase< PointSource >::input_, pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::input_covariances_, pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::mahalanobis_, pcl::Registration< PointSource, PointTarget, float >::max_iterations_, pcl::Registration< PointSource, PointTarget, float >::nr_iterations_, pcl::Registration< PointSource, PointTarget, float >::previous_transformation_, pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::rigid_transformation_estimation_, pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::rotation_epsilon_, pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::searchForNeighbors(), pcl::Registration< PointSource, PointTarget, float >::target_, pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::target_covariances_, pcl::Registration< PointSource, PointTarget, float >::transformation_, pcl::Registration< PointSource, PointTarget, float >::transformation_epsilon_, pcl::transformPointCloud(), pcl::Registration< PointSource, PointTarget, float >::tree_, and pcl::Registration< PointSource, PointTarget, float >::tree_reciprocal_.
void pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::estimateRigidTransformationBFGS | ( | const PointCloudSource & | cloud_src, |
const std::vector< int > & | indices_src, | ||
const PointCloudTarget & | cloud_tgt, | ||
const std::vector< int > & | indices_tgt, | ||
Eigen::Matrix4f & | transformation_matrix | ||
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Estimate a rigid rotation transformation between a source and a target point cloud using an iterative non-linear Levenberg-Marquardt approach.
[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 185 of file gicp.hpp.
References BFGS< FunctorType >::minimizeInit(), BFGS< FunctorType >::minimizeOneStep(), BFGSSpace::NoProgress, BFGS< FunctorType >::parameters, BFGSSpace::Running, BFGSSpace::Success, and BFGS< FunctorType >::testGradient().
Referenced by pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::GeneralizedIterativeClosestPoint().
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Get the number of neighbors used when computing covariances as set by the user.
Definition at line 242 of file gicp.h.
References pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::k_correspondences_.
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Return maximum number of iterations at the optimization step.
Definition at line 253 of file gicp.h.
References pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::max_inner_iterations_.
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Get the rotation epsilon (maximum allowable difference between two consecutive rotations) as set by the user.
Definition at line 227 of file gicp.h.
References pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::rotation_epsilon_.
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Return the minimal rotation gradient threshold for early optimization stop.
Definition at line 275 of file gicp.h.
References pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::rotation_gradient_tolerance_.
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Return the minimal translation gradient threshold for early optimization stop.
Definition at line 264 of file gicp.h.
References pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::translation_gradient_tolerance_.
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Definition at line 199 of file gicp.h.
References pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::mahalanobis_.
Referenced by pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::OptimizationFunctorWithIndices::operator()().
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Search for the closest nearest neighbor of a given point.
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 |
Definition at line 369 of file gicp.h.
References pcl::geometry::distance(), and pcl::Registration< PointSource, PointTarget, float >::tree_.
Referenced by pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::computeTransformation().
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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.
k | the number of neighbors to use when computing covariances |
Definition at line 236 of file gicp.h.
References pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::k_correspondences_.
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Provide a pointer to the input dataset.
cloud | the const boost shared pointer to a PointCloud message |
Reimplemented from pcl::Registration< PointSource, PointTarget, float >.
Definition at line 134 of file gicp.h.
References pcl::Registration< PointSource, PointTarget, float >::getClassName(), pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::input_covariances_, pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::setInputSource(), and pcl::PointCloud< PointT >::size().
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Provide a pointer to the input target (e.g., the point cloud that we want to align the input source to)
[in] | target | the input point cloud target |
Reimplemented from pcl::Registration< PointSource, PointTarget, float >.
Definition at line 166 of file gicp.h.
References pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::setInputTarget(), and pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::target_covariances_.
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Set maximum number of iterations at the optimization step.
[in] | max | maximum number of iterations for the optimizer |
Definition at line 248 of file gicp.h.
References pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::max_inner_iterations_.
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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.
epsilon | the rotation epsilon |
Definition at line 221 of file gicp.h.
References pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::rotation_epsilon_.
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Set the minimal rotation gradient threshold for early optimization stop.
[in] | rotation | gradient threshold in radians |
Definition at line 270 of file gicp.h.
References pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::rotation_gradient_tolerance_.
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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).
[in] | covariances | the input source covariances |
Definition at line 157 of file gicp.h.
References pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::input_covariances_.
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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).
[in] | covariances | the input target covariances |
Definition at line 178 of file gicp.h.
References pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::target_covariances_.
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Set the minimal translation gradient threshold for early optimization stop.
[in] | translation | gradient threshold in meters |
Definition at line 259 of file gicp.h.
References pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::translation_gradient_tolerance_.
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base transformation
Definition at line 297 of file gicp.h.
Referenced by pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::computeTransformation(), and pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::OptimizationFunctorWithIndices::operator()().
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Input cloud points covariances.
Definition at line 312 of file gicp.h.
Referenced by pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::computeTransformation(), pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::setInputSource(), and pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::setSourceCovariances().
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The number of neighbors used for covariances computation.
default: 20
Definition at line 282 of file gicp.h.
Referenced by pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::getCorrespondenceRandomness(), and pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::setCorrespondenceRandomness().
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Mahalanobis matrices holder.
Definition at line 318 of file gicp.h.
Referenced by pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::computeTransformation(), and pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::mahalanobis().
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maximum number of optimizations
Definition at line 321 of file gicp.h.
Referenced by pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::getMaximumOptimizerIterations(), and pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::setMaximumOptimizerIterations().
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Definition at line 397 of file gicp.h.
Referenced by pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::computeTransformation(), and pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::GeneralizedIterativeClosestPoint().
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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 294 of file gicp.h.
Referenced by pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::computeTransformation(), pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::getRotationEpsilon(), and pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::setRotationEpsilon().
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minimal rotation gradient for early optimization stop
Definition at line 327 of file gicp.h.
Referenced by pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::getRotationGradientTolerance(), and pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::setRotationGradientTolerance().
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Target cloud points covariances.
Definition at line 315 of file gicp.h.
Referenced by pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::computeTransformation(), pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::setInputTarget(), and pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::setTargetCovariances().
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Temporary pointer to the source dataset indices.
Definition at line 306 of file gicp.h.
Referenced by pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::OptimizationFunctorWithIndices::operator()().
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Temporary pointer to the target dataset indices.
Definition at line 309 of file gicp.h.
Referenced by pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::OptimizationFunctorWithIndices::operator()().
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Temporary pointer to the source dataset.
Definition at line 300 of file gicp.h.
Referenced by pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::OptimizationFunctorWithIndices::operator()().
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Temporary pointer to the target dataset.
Definition at line 303 of file gicp.h.
Referenced by pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::OptimizationFunctorWithIndices::operator()().
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minimal translation gradient for early optimization stop
Definition at line 324 of file gicp.h.
Referenced by pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::getTranslationGradientTolerance(), and pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::setTranslationGradientTolerance().