Point Cloud Library (PCL)
1.14.1-dev
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A 3D Normal Distribution Transform registration implementation for point cloud data. More...
#include <pcl/registration/ndt.h>
Public Types | |
using | Ptr = shared_ptr< NormalDistributionsTransform< PointSource, PointTarget, Scalar > > |
using | ConstPtr = shared_ptr< const NormalDistributionsTransform< PointSource, PointTarget, Scalar > > |
using | Vector3 = typename Eigen::Matrix< Scalar, 3, 1 > |
using | Matrix4 = typename Registration< PointSource, PointTarget, Scalar >::Matrix4 |
using | Affine3 = typename Eigen::Transform< Scalar, 3, Eigen::Affine > |
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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 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... | |
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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 | |
NormalDistributionsTransform () | |
Constructor. More... | |
~NormalDistributionsTransform () override=default | |
Empty destructor. More... | |
void | setInputTarget (const PointCloudTargetConstPtr &cloud) override |
Provide a pointer to the input target (e.g., the point cloud that we want to align the input source to). More... | |
void | setResolution (float resolution) |
Set/change the voxel grid resolution. More... | |
void | setMinPointPerVoxel (unsigned int min_points_per_voxel) |
Set the minimum number of points required for a cell to be used (must be 3 or greater for covariance calculation). More... | |
float | getResolution () const |
Get voxel grid resolution. More... | |
double | getStepSize () const |
Get the newton line search maximum step length. More... | |
void | setStepSize (double step_size) |
Set/change the newton line search maximum step length. More... | |
double | getOutlierRatio () const |
Get the point cloud outlier ratio. More... | |
double | getOulierRatio () const |
Get the point cloud outlier ratio. More... | |
void | setOutlierRatio (double outlier_ratio) |
Set/change the point cloud outlier ratio. More... | |
void | setOulierRatio (double outlier_ratio) |
Set/change the point cloud outlier ratio. More... | |
double | getTransformationLikelihood () const |
Get the registration alignment likelihood. More... | |
double | getTransformationProbability () const |
Get the registration alignment probability. More... | |
int | getFinalNumIteration () const |
Get the number of iterations required to calculate alignment. More... | |
const TargetGrid & | getTargetCells () const |
Get access to the VoxelGridCovariance generated from target cloud containing point means and covariances. More... | |
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Registration () | |
Empty constructor. More... | |
~Registration () override=default | |
destructor. More... | |
void | setTransformationEstimation (const TransformationEstimationPtr &te) |
Provide a pointer to the transformation estimation object. More... | |
void | setCorrespondenceEstimation (const CorrespondenceEstimationPtr &ce) |
Provide a pointer to the correspondence estimation object. More... | |
virtual void | setInputSource (const PointCloudSourceConstPtr &cloud) |
Provide a pointer to the input source (e.g., the point cloud that we want to align to the target) More... | |
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... | |
Static Public Member Functions | |
static void | convertTransform (const Eigen::Matrix< double, 6, 1 > &x, Affine3 &trans) |
Convert 6 element transformation vector to affine transformation. More... | |
static void | convertTransform (const Eigen::Matrix< double, 6, 1 > &x, Matrix4 &trans) |
Convert 6 element transformation vector to transformation matrix. More... | |
Protected Types | |
using | PointCloudSource = typename Registration< PointSource, PointTarget, Scalar >::PointCloudSource |
using | PointCloudSourcePtr = typename PointCloudSource::Ptr |
using | PointCloudSourceConstPtr = typename PointCloudSource::ConstPtr |
using | PointCloudTarget = typename Registration< PointSource, PointTarget, Scalar >::PointCloudTarget |
using | PointCloudTargetPtr = typename PointCloudTarget::Ptr |
using | PointCloudTargetConstPtr = typename PointCloudTarget::ConstPtr |
using | PointIndicesPtr = PointIndices::Ptr |
using | PointIndicesConstPtr = PointIndices::ConstPtr |
using | TargetGrid = VoxelGridCovariance< PointTarget > |
Typename of searchable voxel grid containing mean and covariance. More... | |
using | TargetGridPtr = TargetGrid * |
Typename of pointer to searchable voxel grid. More... | |
using | TargetGridConstPtr = const TargetGrid * |
Typename of const pointer to searchable voxel grid. More... | |
using | TargetGridLeafConstPtr = typename TargetGrid::LeafConstPtr |
Typename of const pointer to searchable voxel grid leaf. More... | |
Protected Member Functions | |
virtual void | computeTransformation (PointCloudSource &output) |
Estimate the transformation and returns the transformed source (input) as output. More... | |
void | computeTransformation (PointCloudSource &output, const Matrix4 &guess) override |
Estimate the transformation and returns the transformed source (input) as output. More... | |
void | init () |
Initiate covariance voxel structure. More... | |
double | computeDerivatives (Eigen::Matrix< double, 6, 1 > &score_gradient, Eigen::Matrix< double, 6, 6 > &hessian, const PointCloudSource &trans_cloud, const Eigen::Matrix< double, 6, 1 > &transform, bool compute_hessian=true) |
Compute derivatives of likelihood function w.r.t. More... | |
double | updateDerivatives (Eigen::Matrix< double, 6, 1 > &score_gradient, Eigen::Matrix< double, 6, 6 > &hessian, const Eigen::Vector3d &x_trans, const Eigen::Matrix3d &c_inv, bool compute_hessian=true) const |
Compute individual point contributions to derivatives of likelihood function w.r.t. More... | |
void | computeAngleDerivatives (const Eigen::Matrix< double, 6, 1 > &transform, bool compute_hessian=true) |
Precompute angular components of derivatives. More... | |
void | computePointDerivatives (const Eigen::Vector3d &x, bool compute_hessian=true) |
Compute point derivatives. More... | |
void | computeHessian (Eigen::Matrix< double, 6, 6 > &hessian, const PointCloudSource &trans_cloud) |
Compute hessian of likelihood function w.r.t. More... | |
void | updateHessian (Eigen::Matrix< double, 6, 6 > &hessian, const Eigen::Vector3d &x_trans, const Eigen::Matrix3d &c_inv) const |
Compute individual point contributions to hessian of likelihood function w.r.t. More... | |
double | computeStepLengthMT (const Eigen::Matrix< double, 6, 1 > &transform, Eigen::Matrix< double, 6, 1 > &step_dir, double step_init, double step_max, double step_min, double &score, Eigen::Matrix< double, 6, 1 > &score_gradient, Eigen::Matrix< double, 6, 6 > &hessian, PointCloudSource &trans_cloud) |
Compute line search step length and update transform and likelihood derivatives using More-Thuente method. More... | |
bool | updateIntervalMT (double &a_l, double &f_l, double &g_l, double &a_u, double &f_u, double &g_u, double a_t, double f_t, double g_t) const |
Update interval of possible step lengths for More-Thuente method, ![]() | |
double | trialValueSelectionMT (double a_l, double f_l, double g_l, double a_u, double f_u, double g_u, double a_t, double f_t, double g_t) const |
Select new trial value for More-Thuente method. More... | |
double | auxilaryFunction_PsiMT (double a, double f_a, double f_0, double g_0, double mu=1.e-4) const |
Auxiliary function used to determine endpoints of More-Thuente interval. More... | |
double | auxilaryFunction_dPsiMT (double g_a, double g_0, double mu=1.e-4) const |
Auxiliary function derivative used to determine endpoints of More-Thuente interval. More... | |
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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... | |
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 | |
TargetGrid | target_cells_ |
The voxel grid generated from target cloud containing point means and covariances. More... | |
float | resolution_ {1.0f} |
The side length of voxels. More... | |
double | step_size_ {0.1} |
The maximum step length. More... | |
double | outlier_ratio_ {0.55} |
The ratio of outliers of points w.r.t. More... | |
double | gauss_d1_ {0.0} |
The normalization constants used fit the point distribution to a normal distribution, Equation 6.8 [Magnusson 2009]. More... | |
double | gauss_d2_ {0.0} |
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double trans_probability_ | |
double trans_likelihood_ {0.0} | |
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The likelihood score of the transform applied to the input cloud, Equation 6.9 and 6.10 [Magnusson 2009]. More... | |
Eigen::Matrix< double, 8, 4 > | angular_jacobian_ |
Precomputed Angular Gradient. More... | |
Eigen::Matrix< double, 15, 4 > | angular_hessian_ |
Precomputed Angular Hessian. More... | |
Eigen::Matrix< double, 3, 6 > | point_jacobian_ |
The first order derivative of the transformation of a point w.r.t. More... | |
Eigen::Matrix< double, 18, 6 > | point_hessian_ |
The second order derivative of the transformation of a point w.r.t. More... | |
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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... | |
unsigned int | min_number_correspondences_ |
The minimum number of correspondences that the algorithm needs before attempting to estimate the transformation. More... | |
CorrespondencesPtr | correspondences_ |
The set of correspondences determined at this ICP step. More... | |
TransformationEstimationPtr | transformation_estimation_ |
A TransformationEstimation object, used to calculate the 4x4 rigid transformation. More... | |
CorrespondenceEstimationPtr | correspondence_estimation_ |
A CorrespondenceEstimation object, used to estimate correspondences between the source and the target cloud. More... | |
std::vector< 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... | |
A 3D Normal Distribution Transform registration implementation for point cloud data.
using pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::Affine3 = typename Eigen::Transform<Scalar, 3, Eigen::Affine> |
using pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::ConstPtr = shared_ptr<const NormalDistributionsTransform<PointSource, PointTarget, Scalar> > |
using pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::Matrix4 = typename Registration<PointSource, PointTarget, Scalar>::Matrix4 |
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using pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::Ptr = shared_ptr<NormalDistributionsTransform<PointSource, PointTarget, Scalar> > |
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using pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::Vector3 = typename Eigen::Matrix<Scalar, 3, 1> |
pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::NormalDistributionsTransform |
Constructor.
Sets outlier_ratio_ to 0.55, step_size_ to 0.1 and resolution_ to 1.0
Definition at line 47 of file ndt.hpp.
References pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::gauss_d1_, pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::gauss_d2_, pcl::Registration< PointSource, PointTarget, float >::max_iterations_, pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::outlier_ratio_, pcl::Registration< PointSource, PointTarget, float >::reg_name_, pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::resolution_, and pcl::Registration< PointSource, PointTarget, float >::transformation_epsilon_.
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Empty destructor.
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Auxiliary function derivative used to determine endpoints of More-Thuente interval.
[in] | g_a | function gradient at step length a, ![]() |
[in] | g_0 | initial function gradient, ![]() |
[in] | mu | the step length, constant ![]() |
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Auxiliary function used to determine endpoints of More-Thuente interval.
[in] | a | the step length, ![]() |
[in] | f_a | function value at step length a, ![]() |
[in] | f_0 | initial function value, ![]() |
[in] | g_0 | initial function gradient, ![]() |
[in] | mu | the step length, constant ![]() |
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Compute derivatives of likelihood function w.r.t.
the transformation vector.
[out] | score_gradient | the gradient vector of the likelihood function w.r.t. the transformation vector |
[out] | hessian | the hessian matrix of the likelihood function w.r.t. the transformation vector |
[in] | trans_cloud | transformed point cloud |
[in] | transform | the current transform vector |
[in] | compute_hessian | flag to calculate hessian, unnecessary for step calculation. |
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Compute line search step length and update transform and likelihood derivatives using More-Thuente method.
[in] | transform | initial transformation vector, ![]() ![]() |
[in] | step_dir | descent direction, ![]() ![]() |
[in] | step_init | initial step length estimate, ![]() ![]() |
[in] | step_max | maximum step length, ![]() |
[in] | step_min | minimum step length, ![]() |
[out] | score | final score function value, ![]() ![]() |
[in,out] | score_gradient | gradient of score function w.r.t. transformation vector, ![]() ![]() |
[out] | hessian | hessian of score function w.r.t. transformation vector, ![]() ![]() |
[in,out] | trans_cloud | transformed point cloud, ![]() ![]() |
Definition at line 650 of file ndt.hpp.
References pcl::transformPointCloud().
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Estimate the transformation and returns the transformed source (input) as output.
[out] | output | the resultant input transformed point cloud dataset |
[in] | guess | the initial gross estimation of the transformation |
Definition at line 68 of file ndt.hpp.
References pcl::transformPointCloud().
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Convert 6 element transformation vector to affine transformation.
[in] | x | transformation vector of the form [x, y, z, roll, pitch, yaw] |
[out] | trans | affine transform corresponding to given transformation vector |
Definition at line 269 of file ndt.h.
Referenced by pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::convertTransform().
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Convert 6 element transformation vector to transformation matrix.
[in] | x | transformation vector of the form [x, y, z, roll, pitch, yaw] |
[out] | trans | 4x4 transformation matrix corresponding to given transformation vector |
Definition at line 283 of file ndt.h.
References pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::convertTransform().
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Get the number of iterations required to calculate alignment.
Definition at line 244 of file ndt.h.
References pcl::Registration< PointSource, PointTarget, float >::nr_iterations_.
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Get the point cloud outlier ratio.
Definition at line 191 of file ndt.h.
References pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::outlier_ratio_.
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Get the point cloud outlier ratio.
Definition at line 178 of file ndt.h.
References pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::outlier_ratio_.
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Get voxel grid resolution.
Definition at line 151 of file ndt.h.
References pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::resolution_.
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Get the newton line search maximum step length.
Definition at line 160 of file ndt.h.
References pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::step_size_.
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Get access to the VoxelGridCovariance
generated from target cloud containing point means and covariances.
Set the input target cloud before calling this. Useful for debugging, e.g.
Definition at line 258 of file ndt.h.
References pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::target_cells_.
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Get the registration alignment likelihood.
Definition at line 222 of file ndt.h.
References pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::trans_likelihood_.
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Get the registration alignment probability.
Definition at line 235 of file ndt.h.
References pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::trans_likelihood_.
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Initiate covariance voxel structure.
Definition at line 329 of file ndt.h.
References pcl::VoxelGridCovariance< PointT >::filter(), pcl::VoxelGridCovariance< PointT >::getCentroids(), pcl::Registration< PointSource, PointTarget, float >::getClassName(), pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::resolution_, pcl::PCLBase< PointT >::setInputCloud(), pcl::VoxelGrid< PointT >::setLeafSize(), pcl::Registration< PointSource, PointTarget, float >::target_, and pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::target_cells_.
Referenced by pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::setInputTarget(), and pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::setResolution().
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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] | cloud | the input point cloud target |
Reimplemented from pcl::Registration< PointSource, PointTarget, float >.
Definition at line 114 of file ndt.h.
References pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::init(), and pcl::Registration< PointSource, PointTarget, Scalar >::setInputTarget().
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Set the minimum number of points required for a cell to be used (must be 3 or greater for covariance calculation).
Calls the function of the underlying VoxelGridCovariance. This function must be called before setInputTarget
and setResolution
.
[in] | min_points_per_voxel | the minimum number of points required for a voxel to be used |
Definition at line 142 of file ndt.h.
References pcl::VoxelGridCovariance< PointT >::setMinPointPerVoxel(), and pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::target_cells_.
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Set/change the point cloud outlier ratio.
[in] | outlier_ratio | outlier ratio |
Definition at line 213 of file ndt.h.
References pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::outlier_ratio_.
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Set/change the point cloud outlier ratio.
[in] | outlier_ratio | outlier ratio |
Definition at line 200 of file ndt.h.
References pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::outlier_ratio_.
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Set/change the voxel grid resolution.
[in] | resolution | side length of voxels |
Definition at line 124 of file ndt.h.
References pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::init(), pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::resolution_, and pcl::Registration< PointSource, PointTarget, float >::target_.
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Set/change the newton line search maximum step length.
[in] | step_size | maximum step length |
Definition at line 169 of file ndt.h.
References pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::step_size_.
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Select new trial value for More-Thuente method.
[in] | a_l | first endpoint of interval ![]() ![]() |
[in] | f_l | value at first endpoint, ![]() |
[in] | g_l | derivative at first endpoint, ![]() |
[in] | a_u | second endpoint of interval ![]() ![]() |
[in] | f_u | value at second endpoint, ![]() |
[in] | g_u | derivative at second endpoint, ![]() |
[in] | a_t | previous trial value, ![]() |
[in] | f_t | value at previous trial value, ![]() |
[in] | g_t | derivative at previous trial value, ![]() |
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Compute individual point contributions to derivatives of likelihood function w.r.t.
the transformation vector.
[in,out] | score_gradient | the gradient vector of the likelihood function w.r.t. the transformation vector |
[in,out] | hessian | the hessian matrix of the likelihood function w.r.t. the transformation vector |
[in] | x_trans | transformed point minus mean of occupied covariance voxel |
[in] | c_inv | covariance of occupied covariance voxel |
[in] | compute_hessian | flag to calculate hessian, unnecessary for step calculation. |
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Compute individual point contributions to hessian of likelihood function w.r.t.
the transformation vector.
[in,out] | hessian | the hessian matrix of the likelihood function w.r.t. the transformation vector |
[in] | x_trans | transformed point minus mean of occupied covariance voxel |
[in] | c_inv | covariance of occupied covariance voxel |
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Update interval of possible step lengths for More-Thuente method, in More-Thuente (1994)
[in,out] | a_l | first endpoint of interval ![]() ![]() |
[in,out] | f_l | value at first endpoint, ![]() ![]() ![]() |
[in,out] | g_l | derivative at first endpoint, ![]() ![]() ![]() |
[in,out] | a_u | second endpoint of interval ![]() ![]() |
[in,out] | f_u | value at second endpoint, ![]() ![]() ![]() |
[in,out] | g_u | derivative at second endpoint, ![]() ![]() ![]() |
[in] | a_t | trial value, ![]() |
[in] | f_t | value at trial value, ![]() ![]() ![]() |
[in] | g_t | derivative at trial value, ![]() ![]() ![]() |
union { ... } |
The likelihood score of the transform applied to the input cloud, Equation 6.9 and 6.10 [Magnusson 2009].
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The normalization constants used fit the point distribution to a normal distribution, Equation 6.8 [Magnusson 2009].
Definition at line 597 of file ndt.h.
Referenced by pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::NormalDistributionsTransform().
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Definition at line 597 of file ndt.h.
Referenced by pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::NormalDistributionsTransform().
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The ratio of outliers of points w.r.t.
a normal distribution, Equation 6.7 [Magnusson 2009].
Definition at line 593 of file ndt.h.
Referenced by pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::getOulierRatio(), pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::getOutlierRatio(), pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::NormalDistributionsTransform(), pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::setOulierRatio(), and pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::setOutlierRatio().
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The side length of voxels.
Definition at line 586 of file ndt.h.
Referenced by pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::getResolution(), pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::init(), pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::NormalDistributionsTransform(), and pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::setResolution().
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The maximum step length.
Definition at line 589 of file ndt.h.
Referenced by pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::getStepSize(), and pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::setStepSize().
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The voxel grid generated from target cloud containing point means and covariances.
Definition at line 583 of file ndt.h.
Referenced by pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::getTargetCells(), pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::init(), and pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::setMinPointPerVoxel().
double pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::trans_likelihood_ {0.0} |
double pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::trans_probability_ |