icp.h
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#pragma once
// PCL includes
#include <pcl/make_shared.h>
#include <pcl/sample_consensus/ransac.h>
#include <pcl/sample_consensus/sac_model_registration.h>
#include <pcl/registration/registration.h>
#include <pcl/registration/transformation_estimation_svd.h>
#include <pcl/registration/transformation_estimation_point_to_plane_lls.h>
#include <pcl/registration/transformation_estimation_symmetric_point_to_plane_lls.h>
#include <pcl/registration/correspondence_estimation.h>
#include <pcl/registration/default_convergence_criteria.h>
namespace pcl
{
/** \brief @b IterativeClosestPoint provides a base implementation of the Iterative Closest Point algorithm.
* The transformation is estimated based on Singular Value Decomposition (SVD).
*
* The algorithm has several termination criteria:
*
* <ol>
* <li>Number of iterations has reached the maximum user imposed number of iterations (via \ref setMaximumIterations)</li>
* <li>The epsilon (difference) between the previous transformation and the current estimated transformation is smaller than an user imposed value (via \ref setTransformationEpsilon)</li>
* <li>The sum of Euclidean squared errors is smaller than a user defined threshold (via \ref setEuclideanFitnessEpsilon)</li>
* </ol>
*
*
* Usage example:
* \code
* IterativeClosestPoint<PointXYZ, PointXYZ> icp;
* // Set the input source and target
* icp.setInputCloud (cloud_source);
* icp.setInputTarget (cloud_target);
*
* // Set the max correspondence distance to 5cm (e.g., correspondences with higher distances will be ignored)
* icp.setMaxCorrespondenceDistance (0.05);
* // Set the maximum number of iterations (criterion 1)
* icp.setMaximumIterations (50);
* // Set the transformation epsilon (criterion 2)
* icp.setTransformationEpsilon (1e-8);
* // Set the euclidean distance difference epsilon (criterion 3)
* icp.setEuclideanFitnessEpsilon (1);
*
* // Perform the alignment
* icp.align (cloud_source_registered);
*
* // Obtain the transformation that aligned cloud_source to cloud_source_registered
* Eigen::Matrix4f transformation = icp.getFinalTransformation ();
* \endcode
*
* \author Radu B. Rusu, Michael Dixon
* \ingroup registration
*/
template <typename PointSource, typename PointTarget, typename Scalar = float>
class IterativeClosestPoint : public Registration<PointSource, PointTarget, Scalar>
{
public:
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 Ptr = shared_ptr<IterativeClosestPoint<PointSource, PointTarget, Scalar> >;
using ConstPtr = shared_ptr<const IterativeClosestPoint<PointSource, PointTarget, Scalar> >;
using Registration<PointSource, PointTarget, Scalar>::reg_name_;
using Registration<PointSource, PointTarget, Scalar>::getClassName;
using Registration<PointSource, PointTarget, Scalar>::input_;
using Registration<PointSource, PointTarget, Scalar>::indices_;
using Registration<PointSource, PointTarget, Scalar>::target_;
using Registration<PointSource, PointTarget, Scalar>::nr_iterations_;
using Registration<PointSource, PointTarget, Scalar>::max_iterations_;
using Registration<PointSource, PointTarget, Scalar>::previous_transformation_;
using Registration<PointSource, PointTarget, Scalar>::final_transformation_;
using Registration<PointSource, PointTarget, Scalar>::transformation_;
using Registration<PointSource, PointTarget, Scalar>::transformation_epsilon_;
using Registration<PointSource, PointTarget, Scalar>::transformation_rotation_epsilon_;
using Registration<PointSource, PointTarget, Scalar>::converged_;
using Registration<PointSource, PointTarget, Scalar>::corr_dist_threshold_;
using Registration<PointSource, PointTarget, Scalar>::inlier_threshold_;
using Registration<PointSource, PointTarget, Scalar>::min_number_correspondences_;
using Registration<PointSource, PointTarget, Scalar>::update_visualizer_;
using Registration<PointSource, PointTarget, Scalar>::euclidean_fitness_epsilon_;
using Registration<PointSource, PointTarget, Scalar>::correspondences_;
using Registration<PointSource, PointTarget, Scalar>::transformation_estimation_;
using Registration<PointSource, PointTarget, Scalar>::correspondence_estimation_;
using Registration<PointSource, PointTarget, Scalar>::correspondence_rejectors_;
typename pcl::registration::DefaultConvergenceCriteria<Scalar>::Ptr convergence_criteria_;
using Matrix4 = typename Registration<PointSource, PointTarget, Scalar>::Matrix4;
/** \brief Empty constructor. */
IterativeClosestPoint ()
: x_idx_offset_ (0)
, y_idx_offset_ (0)
, z_idx_offset_ (0)
, nx_idx_offset_ (0)
, ny_idx_offset_ (0)
, nz_idx_offset_ (0)
, use_reciprocal_correspondence_ (false)
, source_has_normals_ (false)
, target_has_normals_ (false)
{
reg_name_ = "IterativeClosestPoint";
transformation_estimation_.reset (new pcl::registration::TransformationEstimationSVD<PointSource, PointTarget, Scalar> ());
correspondence_estimation_.reset (new pcl::registration::CorrespondenceEstimation<PointSource, PointTarget, Scalar>);
convergence_criteria_.reset(new pcl::registration::DefaultConvergenceCriteria<Scalar> (nr_iterations_, transformation_, *correspondences_));
};
/** \brief Empty destructor */
~IterativeClosestPoint () {}
/** \brief Returns a pointer to the DefaultConvergenceCriteria used by the IterativeClosestPoint class.
* This allows to check the convergence state after the align() method as well as to configure
* DefaultConvergenceCriteria's parameters not available through the ICP API before the align()
* method is called. Please note that the align method sets max_iterations_,
* euclidean_fitness_epsilon_ and transformation_epsilon_ and therefore overrides the default / set
* values of the DefaultConvergenceCriteria instance.
* \return Pointer to the IterativeClosestPoint's DefaultConvergenceCriteria.
*/
inline typename pcl::registration::DefaultConvergenceCriteria<Scalar>::Ptr
getConvergeCriteria ()
{
return convergence_criteria_;
}
/** \brief Provide a pointer to the input source
* (e.g., the point cloud that we want to align to the target)
*
* \param[in] cloud the input point cloud source
*/
void
setInputSource (const PointCloudSourceConstPtr &cloud) override
{
Registration<PointSource, PointTarget, Scalar>::setInputSource (cloud);
const auto fields = pcl::getFields<PointSource> ();
source_has_normals_ = false;
for (const auto &field : fields)
{
if (field.name == "x") x_idx_offset_ = field.offset;
else if (field.name == "y") y_idx_offset_ = field.offset;
else if (field.name == "z") z_idx_offset_ = field.offset;
else if (field.name == "normal_x")
{
source_has_normals_ = true;
nx_idx_offset_ = field.offset;
}
else if (field.name == "normal_y")
{
source_has_normals_ = true;
ny_idx_offset_ = field.offset;
}
else if (field.name == "normal_z")
{
source_has_normals_ = true;
nz_idx_offset_ = field.offset;
}
}
}
/** \brief Provide a pointer to the input target
* (e.g., the point cloud that we want to align to the target)
*
* \param[in] cloud the input point cloud target
*/
void
setInputTarget (const PointCloudTargetConstPtr &cloud) override
{
Registration<PointSource, PointTarget, Scalar>::setInputTarget (cloud);
const auto fields = pcl::getFields<PointSource> ();
target_has_normals_ = false;
for (const auto &field : fields)
{
if (field.name == "normal_x" || field.name == "normal_y" || field.name == "normal_z")
{
target_has_normals_ = true;
break;
}
}
}
/** \brief Set whether to use reciprocal correspondence or not
*
* \param[in] use_reciprocal_correspondence whether to use reciprocal correspondence or not
*/
inline void
setUseReciprocalCorrespondences (bool use_reciprocal_correspondence)
{
use_reciprocal_correspondence_ = use_reciprocal_correspondence;
}
/** \brief Obtain whether reciprocal correspondence are used or not */
inline bool
getUseReciprocalCorrespondences () const
{
return (use_reciprocal_correspondence_);
}
protected:
/** \brief Apply a rigid transform to a given dataset. Here we check whether whether
* the dataset has surface normals in addition to XYZ, and rotate normals as well.
* \param[in] input the input point cloud
* \param[out] output the resultant output point cloud
* \param[in] transform a 4x4 rigid transformation
* \note Can be used with cloud_in equal to cloud_out
*/
virtual void
transformCloud (const PointCloudSource &input,
PointCloudSource &output,
const Matrix4 &transform);
/** \brief Rigid transformation computation method with initial guess.
* \param output the transformed input point cloud dataset using the rigid transformation found
* \param guess the initial guess of the transformation to compute
*/
void
computeTransformation (PointCloudSource &output, const Matrix4 &guess) override;
/** \brief Looks at the Estimators and Rejectors and determines whether their blob-setter methods need to be called */
virtual void
determineRequiredBlobData ();
/** \brief XYZ fields offset. */
std::size_t x_idx_offset_, y_idx_offset_, z_idx_offset_;
/** \brief Normal fields offset. */
std::size_t nx_idx_offset_, ny_idx_offset_, nz_idx_offset_;
/** \brief The correspondence type used for correspondence estimation. */
bool use_reciprocal_correspondence_;
/** \brief Internal check whether source dataset has normals or not. */
bool source_has_normals_;
/** \brief Internal check whether target dataset has normals or not. */
bool target_has_normals_;
/** \brief Checks for whether estimators and rejectors need various data */
bool need_source_blob_, need_target_blob_;
};
/** \brief @b IterativeClosestPointWithNormals is a special case of
* IterativeClosestPoint, that uses a transformation estimated based on
* Point to Plane distances by default.
*
* By default, this implementation uses the traditional point to plane objective
* and computes point to plane distances using the normals of the target point
* cloud. It also provides the option (through setUseSymmetricObjective) of
* using the symmetric objective function of [Rusinkiewicz 2019]. This objective
* uses the normals of both the source and target point cloud and has a similar
* computational cost to the traditional point to plane objective while also
* offering improved convergence speed and a wider basin of convergence.
*
* Note that this implementation not demean the point clouds which can lead
* to increased numerical error. If desired, a user can demean the point cloud,
* run iterative closest point, and composite the resulting ICP transformation
* with the translations from demeaning to obtain a transformation between
* the original point clouds.
*
* \author Radu B. Rusu, Matthew Cong
* \ingroup registration
*/
template <typename PointSource, typename PointTarget, typename Scalar = float>
class IterativeClosestPointWithNormals : public IterativeClosestPoint<PointSource, PointTarget, Scalar>
{
public:
using PointCloudSource = typename IterativeClosestPoint<PointSource, PointTarget, Scalar>::PointCloudSource;
using PointCloudTarget = typename IterativeClosestPoint<PointSource, PointTarget, Scalar>::PointCloudTarget;
using Matrix4 = typename IterativeClosestPoint<PointSource, PointTarget, Scalar>::Matrix4;
using IterativeClosestPoint<PointSource, PointTarget, Scalar>::reg_name_;
using IterativeClosestPoint<PointSource, PointTarget, Scalar>::transformation_estimation_;
using IterativeClosestPoint<PointSource, PointTarget, Scalar>::correspondence_rejectors_;
using Ptr = shared_ptr<IterativeClosestPoint<PointSource, PointTarget, Scalar> >;
using ConstPtr = shared_ptr<const IterativeClosestPoint<PointSource, PointTarget, Scalar> >;
/** \brief Empty constructor. */
IterativeClosestPointWithNormals ()
{
reg_name_ = "IterativeClosestPointWithNormals";
setUseSymmetricObjective (false);
setEnforceSameDirectionNormals (true);
//correspondence_rejectors_.add
};
/** \brief Empty destructor */
virtual ~IterativeClosestPointWithNormals () {}
/** \brief Set whether to use a symmetric objective function or not
*
* \param[in] use_symmetric_objective whether to use a symmetric objective function or not
*/
inline void
setUseSymmetricObjective (bool use_symmetric_objective)
{
use_symmetric_objective_ = use_symmetric_objective;
if (use_symmetric_objective_)
{
auto symmetric_transformation_estimation = pcl::make_shared<pcl::registration::TransformationEstimationSymmetricPointToPlaneLLS<PointSource, PointTarget, Scalar> > ();
symmetric_transformation_estimation->setEnforceSameDirectionNormals (enforce_same_direction_normals_);
transformation_estimation_ = symmetric_transformation_estimation;
}
else
{
transformation_estimation_.reset (new pcl::registration::TransformationEstimationPointToPlaneLLS<PointSource, PointTarget, Scalar> ());
}
}
/** \brief Obtain whether a symmetric objective is used or not */
inline bool
getUseSymmetricObjective () const
{
return use_symmetric_objective_;
}
/** \brief Set whether or not to negate source or target normals on a per-point basis such that they point in the same direction. Only applicable to the symmetric objective function.
*
* \param[in] enforce_same_direction_normals whether to negate source or target normals on a per-point basis such that they point in the same direction.
*/
inline void
setEnforceSameDirectionNormals (bool enforce_same_direction_normals)
{
enforce_same_direction_normals_ = enforce_same_direction_normals;
auto symmetric_transformation_estimation = boost::dynamic_pointer_cast<pcl::registration::TransformationEstimationSymmetricPointToPlaneLLS<PointSource, PointTarget, Scalar> >(transformation_estimation_);
if (symmetric_transformation_estimation)
symmetric_transformation_estimation->setEnforceSameDirectionNormals (enforce_same_direction_normals_);
}
/** \brief Obtain whether source or target normals are negated on a per-point basis such that they point in the same direction or not */
inline bool
getEnforceSameDirectionNormals () const
{
return enforce_same_direction_normals_;
}
protected:
/** \brief Apply a rigid transform to a given dataset
* \param[in] input the input point cloud
* \param[out] output the resultant output point cloud
* \param[in] transform a 4x4 rigid transformation
* \note Can be used with cloud_in equal to cloud_out
*/
virtual void
transformCloud (const PointCloudSource &input,
PointCloudSource &output,
const Matrix4 &transform);
/** \brief Type of objective function (asymmetric vs. symmetric) used for transform estimation */
bool use_symmetric_objective_;
/** \brief Whether or not to negate source and/or target normals such that they point in the same direction in the symmetric objective function */
bool enforce_same_direction_normals_;
};
}
#include <pcl/registration/impl/icp.hpp>
pcl::IterativeClosestPoint< PointSource, PointTarget >::Ptr
shared_ptr< IterativeClosestPoint< PointSource, PointTarget, float > > Ptr
Definition: icp.h:109
pcl::IterativeClosestPointWithNormals::use_symmetric_objective_
bool use_symmetric_objective_
Type of objective function (asymmetric vs.
Definition: icp.h:397
pcl::IterativeClosestPoint::getConvergeCriteria
pcl::registration::DefaultConvergenceCriteria< Scalar >::Ptr getConvergeCriteria()
Returns a pointer to the DefaultConvergenceCriteria used by the IterativeClosestPoint class.
Definition: icp.h:168
pcl::Registration::correspondences_
CorrespondencesPtr correspondences_
The set of correspondences determined at this ICP step.
Definition: registration.h:556
pcl
This file defines compatibility wrappers for low level I/O functions.
Definition: convolution.h:45
pcl::registration::TransformationEstimationSVD
TransformationEstimationSVD implements SVD-based estimation of the transformation aligning the given ...
Definition: transformation_estimation_svd.h:58
pcl::IterativeClosestPoint::setUseReciprocalCorrespondences
void setUseReciprocalCorrespondences(bool use_reciprocal_correspondence)
Set whether to use reciprocal correspondence or not.
Definition: icp.h:233
pcl::Registration::setInputTarget
virtual void setInputTarget(const PointCloudTargetConstPtr &cloud)
Provide a pointer to the input target (e.g., the point cloud that we want to align the input source t...
Definition: registration.hpp:43
pcl::Registration
Registration represents the base registration class for general purpose, ICP-like methods.
Definition: registration.h:60
pcl::IterativeClosestPoint::y_idx_offset_
std::size_t y_idx_offset_
Definition: icp.h:271
pcl::registration::DefaultConvergenceCriteria
DefaultConvergenceCriteria represents an instantiation of ConvergenceCriteria, and implements the fol...
Definition: default_convergence_criteria.h:65
pcl::Registration::setInputSource
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)
Definition: registration.h:190
pcl::IterativeClosestPoint::need_target_blob_
bool need_target_blob_
Definition: icp.h:285
pcl::IterativeClosestPoint< PointSource, PointTarget >::PointCloudSourcePtr
typename PointCloudSource::Ptr PointCloudSourcePtr
Definition: icp.h:99
pcl::IterativeClosestPointWithNormals::transformCloud
virtual void transformCloud(const PointCloudSource &input, PointCloudSource &output, const Matrix4 &transform)
Apply a rigid transform to a given dataset.
Definition: icp.hpp:291
pcl::IterativeClosestPointWithNormals::IterativeClosestPointWithNormals
IterativeClosestPointWithNormals()
Empty constructor.
Definition: icp.h:325
pcl::IterativeClosestPoint
IterativeClosestPoint provides a base implementation of the Iterative Closest Point algorithm.
Definition: icp.h:95
pcl::IterativeClosestPointWithNormals::setUseSymmetricObjective
void setUseSymmetricObjective(bool use_symmetric_objective)
Set whether to use a symmetric objective function or not.
Definition: icp.h:341
pcl::Registration::transformation_
Matrix4 transformation_
The transformation matrix estimated by the registration method.
Definition: registration.h:515
pcl::IterativeClosestPoint::use_reciprocal_correspondence_
bool use_reciprocal_correspondence_
The correspondence type used for correspondence estimation.
Definition: icp.h:277
pcl::IterativeClosestPointWithNormals::enforce_same_direction_normals_
bool enforce_same_direction_normals_
Whether or not to negate source and/or target normals such that they point in the same direction in t...
Definition: icp.h:399
pcl::IterativeClosestPoint< PointSource, PointTarget >::PointCloudTarget
typename Registration< PointSource, PointTarget, float >::PointCloudTarget PointCloudTarget
Definition: icp.h:102
pcl::PCLBase< PointSource >::PointIndicesConstPtr
PointIndices::ConstPtr PointIndicesConstPtr
Definition: pcl_base.h:77
pcl::IterativeClosestPoint::setInputSource
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)
Definition: icp.h:179
pcl::IterativeClosestPointWithNormals::getUseSymmetricObjective
bool getUseSymmetricObjective() const
Obtain whether a symmetric objective is used or not.
Definition: icp.h:358
pcl::PointCloud< PointSource >
pcl::Registration::Matrix4
Eigen::Matrix< Scalar, 4, 4 > Matrix4
Definition: registration.h:63
pcl::IterativeClosestPoint::determineRequiredBlobData
virtual void determineRequiredBlobData()
Looks at the Estimators and Rejectors and determines whether their blob-setter methods need to be cal...
Definition: icp.hpp:254
pcl::IterativeClosestPoint< PointSource, PointTarget >::PointCloudSource
typename Registration< PointSource, PointTarget, float >::PointCloudSource PointCloudSource
Definition: icp.h:98
pcl::registration::DefaultConvergenceCriteria::Ptr
shared_ptr< DefaultConvergenceCriteria< Scalar > > Ptr
Definition: default_convergence_criteria.h:68
pcl::IterativeClosestPointWithNormals::~IterativeClosestPointWithNormals
virtual ~IterativeClosestPointWithNormals()
Empty destructor.
Definition: icp.h:334
pcl::registration::TransformationEstimationSymmetricPointToPlaneLLS::setEnforceSameDirectionNormals
void setEnforceSameDirectionNormals(bool enforce_same_direction_normals)
Set whether or not to negate source or target normals on a per-point basis such that they point in th...
Definition: transformation_estimation_symmetric_point_to_plane_lls.hpp:193
pcl::IterativeClosestPoint< PointSource, PointTarget >::PointCloudTargetConstPtr
typename PointCloudTarget::ConstPtr PointCloudTargetConstPtr
Definition: icp.h:104
pcl::IterativeClosestPoint::nx_idx_offset_
std::size_t nx_idx_offset_
Normal fields offset.
Definition: icp.h:274
pcl::PointIndices::ConstPtr
shared_ptr< const ::pcl::PointIndices > ConstPtr
Definition: PointIndices.h:23
pcl::IterativeClosestPointWithNormals
IterativeClosestPointWithNormals is a special case of IterativeClosestPoint, that uses a transformati...
Definition: icp.h:310
pcl::IterativeClosestPoint::setInputTarget
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)
Definition: icp.h:213
pcl::IterativeClosestPointWithNormals::getEnforceSameDirectionNormals
bool getEnforceSameDirectionNormals() const
Obtain whether source or target normals are negated on a per-point basis such that they point in the ...
Definition: icp.h:378
pcl::IterativeClosestPointWithNormals::setEnforceSameDirectionNormals
void setEnforceSameDirectionNormals(bool enforce_same_direction_normals)
Set whether or not to negate source or target normals on a per-point basis such that they point in th...
Definition: icp.h:368
pcl::IterativeClosestPoint::transformCloud
virtual void transformCloud(const PointCloudSource &input, PointCloudSource &output, const Matrix4 &transform)
Apply a rigid transform to a given dataset.
Definition: icp.hpp:49
pcl::IterativeClosestPoint::convergence_criteria_
pcl::registration::DefaultConvergenceCriteria< Scalar >::Ptr convergence_criteria_
Definition: icp.h:135
pcl::IterativeClosestPoint< PointSource, PointTarget >::PointCloudSourceConstPtr
typename PointCloudSource::ConstPtr PointCloudSourceConstPtr
Definition: icp.h:100
pcl::IterativeClosestPoint::ny_idx_offset_
std::size_t ny_idx_offset_
Definition: icp.h:274
pcl::registration::CorrespondenceEstimation
CorrespondenceEstimation represents the base class for determining correspondences between target and...
Definition: correspondence_estimation.h:361
pcl::registration::TransformationEstimationSymmetricPointToPlaneLLS
TransformationEstimationSymmetricPointToPlaneLLS implements a Linear Least Squares (LLS) approximatio...
Definition: transformation_estimation_symmetric_point_to_plane_lls.h:61
pcl::IterativeClosestPoint::IterativeClosestPoint
IterativeClosestPoint()
Empty constructor.
Definition: icp.h:139
pcl::PointIndices::Ptr
shared_ptr< ::pcl::PointIndices > Ptr
Definition: PointIndices.h:22
pcl::IterativeClosestPoint::z_idx_offset_
std::size_t z_idx_offset_
Definition: icp.h:271
pcl::Registration::nr_iterations_
int nr_iterations_
The number of iterations the internal optimization ran for (used internally).
Definition: registration.h:498
pcl::IterativeClosestPoint::nz_idx_offset_
std::size_t nz_idx_offset_
Definition: icp.h:274
pcl::IterativeClosestPoint::computeTransformation
void computeTransformation(PointCloudSource &output, const Matrix4 &guess) override
Rigid transformation computation method with initial guess.
Definition: icp.hpp:119
pcl::IterativeClosestPoint::need_source_blob_
bool need_source_blob_
Checks for whether estimators and rejectors need various data.
Definition: icp.h:285
pcl::IterativeClosestPointWithNormals::PointCloudSource
typename IterativeClosestPoint< PointSource, PointTarget, Scalar >::PointCloudSource PointCloudSource
Definition: icp.h:313
pcl::IterativeClosestPoint::~IterativeClosestPoint
~IterativeClosestPoint()
Empty destructor.
Definition: icp.h:157
pcl::IterativeClosestPointWithNormals::Matrix4
typename IterativeClosestPoint< PointSource, PointTarget, Scalar >::Matrix4 Matrix4
Definition: icp.h:315
pcl::PCLBase< PointSource >::PointIndicesPtr
PointIndices::Ptr PointIndicesPtr
Definition: pcl_base.h:76
pcl::IterativeClosestPoint< PointSource, PointTarget >::PointCloudTargetPtr
typename PointCloudTarget::Ptr PointCloudTargetPtr
Definition: icp.h:103
pcl::Registration::transformation_estimation_
TransformationEstimationPtr transformation_estimation_
A TransformationEstimation object, used to calculate the 4x4 rigid transformation.
Definition: registration.h:559
pcl::IterativeClosestPoint::source_has_normals_
bool source_has_normals_
Internal check whether source dataset has normals or not.
Definition: icp.h:280
pcl::IterativeClosestPoint::target_has_normals_
bool target_has_normals_
Internal check whether target dataset has normals or not.
Definition: icp.h:282
pcl::IterativeClosestPoint< PointSource, PointTarget >::Matrix4
typename Registration< PointSource, PointTarget, float >::Matrix4 Matrix4
Definition: icp.h:136
pcl::IterativeClosestPoint< PointSource, PointTarget >::ConstPtr
shared_ptr< const IterativeClosestPoint< PointSource, PointTarget, float > > ConstPtr
Definition: icp.h:110
pcl::IterativeClosestPoint::getUseReciprocalCorrespondences
bool getUseReciprocalCorrespondences() const
Obtain whether reciprocal correspondence are used or not.
Definition: icp.h:240
pcl::Registration::reg_name_
std::string reg_name_
The registration method name.
Definition: registration.h:489
pcl::registration::TransformationEstimationPointToPlaneLLS
TransformationEstimationPointToPlaneLLS implements a Linear Least Squares (LLS) approximation for min...
Definition: transformation_estimation_point_to_plane_lls.h:63
pcl::Registration::correspondence_estimation_
CorrespondenceEstimationPtr correspondence_estimation_
A CorrespondenceEstimation object, used to estimate correspondences between the source and the target...
Definition: registration.h:562
pcl::shared_ptr
boost::shared_ptr< T > shared_ptr
Alias for boost::shared_ptr.
Definition: pcl_macros.h:90
pcl::IterativeClosestPoint::x_idx_offset_
std::size_t x_idx_offset_
XYZ fields offset.
Definition: icp.h:271