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IterativeClosestPointNonLinear is an ICP variant that uses Levenberg-Marquardt optimization backend. More...

#include <pcl/registration/icp_nl.h>

Public Types

using Ptr = shared_ptr< IterativeClosestPointNonLinear< PointSource, PointTarget, Scalar > >
using ConstPtr = shared_ptr< const IterativeClosestPointNonLinear< PointSource, PointTarget, Scalar > >
using Matrix4 = typename Registration< PointSource, PointTarget, Scalar >::Matrix4
- 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

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

Additional Inherited Members

- Public Attributes inherited from pcl::IterativeClosestPoint< PointSource, PointTarget, float >
pcl::registration::DefaultConvergenceCriteria< float >::Ptr convergence_criteria_
- 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 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...
int min_number_correspondences_
The minimum number of correspondences that the algorithm needs before attempting to estimate the transformation. More...
CorrespondencesPtr correspondences_
The set of correspondences determined at this ICP step. More...
TransformationEstimationPtr transformation_estimation_
A TransformationEstimation object, used to calculate the 4x4 rigid transformation. More...
CorrespondenceEstimationPtr correspondence_estimation_
A CorrespondenceEstimation object, used to estimate correspondences between the source and the target cloud. More...
std::vector< CorrespondenceRejectorPtr > correspondence_rejectors_
The list of correspondence rejectors to use. More...
bool target_cloud_updated_
Variable that stores whether we have a new target cloud, meaning we need to pre-process it again. More...
bool source_cloud_updated_
Variable that stores whether we have a new source cloud, meaning we need to pre-process it again. More...
bool force_no_recompute_
A flag which, if set, means the tree operating on the target cloud will never be recomputed. More...
bool force_no_recompute_reciprocal_
A flag which, if set, means the tree operating on the source cloud will never be recomputed. More...
std::function< UpdateVisualizerCallbackSignature > update_visualizer_
Callback function to update intermediate source point cloud position during it's registration to the target point cloud. More...
- Protected Attributes inherited from pcl::PCLBase< PointSource >
PointCloudConstPtr input_
The input point cloud dataset. More...
IndicesPtr indices_
A pointer to the vector of point indices to use. More...
bool use_indices_
Set to true if point indices are used. More...
bool fake_indices_
If no set of indices are given, we construct a set of fake indices that mimic the input PointCloud. More...

Detailed Description

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

IterativeClosestPointNonLinear is an ICP variant that uses Levenberg-Marquardt optimization backend.

The resultant transformation is optimized as a quaternion.

The algorithm has several termination criteria:

  1. Number of iterations has reached the maximum user imposed number of iterations (via setMaximumIterations)
  2. The epsilon (difference) between the previous transformation and the current estimated transformation is smaller than an user imposed value (via setTransformationEpsilon)
  3. The sum of Euclidean squared errors is smaller than a user defined threshold (via setEuclideanFitnessEpsilon)
Author
Radu B. Rusu, Michael Dixon

Definition at line 67 of file icp_nl.h.

Member Typedef Documentation

ConstPtr

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

Definition at line 80 of file icp_nl.h.

Matrix4

template<typename PointSource , typename PointTarget , typename Scalar = float>
using pcl::IterativeClosestPointNonLinear< PointSource, PointTarget, Scalar >::Matrix4 = typename Registration<PointSource, PointTarget, Scalar>::Matrix4

Definition at line 82 of file icp_nl.h.

Ptr

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

Definition at line 78 of file icp_nl.h.

Constructor & Destructor Documentation

IterativeClosestPointNonLinear()

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

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

© 2009–2012, Willow Garage, Inc.
© 2012–, Open Perception, Inc.
Licensed under the BSD License.
https://pointclouds.org/documentation/classpcl_1_1_iterative_closest_point_non_linear.html