icp.h
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#ifndef PCL_ICP_H_
#define PCL_ICP_H_
// PCL includes
#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/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:
typedef typename Registration<PointSource, PointTarget, Scalar>::PointCloudSource PointCloudSource;
typedef typename PointCloudSource::Ptr PointCloudSourcePtr;
typedef typename PointCloudSource::ConstPtr PointCloudSourceConstPtr;
typedef typename Registration<PointSource, PointTarget, Scalar>::PointCloudTarget PointCloudTarget;
typedef typename PointCloudTarget::Ptr PointCloudTargetPtr;
typedef typename PointCloudTarget::ConstPtr PointCloudTargetConstPtr;
typedef PointIndices::Ptr PointIndicesPtr;
typedef PointIndices::ConstPtr PointIndicesConstPtr;
typedef boost::shared_ptr<IterativeClosestPoint<PointSource, PointTarget, Scalar> > Ptr;
typedef boost::shared_ptr<const IterativeClosestPoint<PointSource, PointTarget, Scalar> > ConstPtr;
using Registration<PointSource, PointTarget, Scalar>::reg_name_;
using Registration<PointSource, PointTarget, Scalar>::getClassName;
using Registration<PointSource, PointTarget, Scalar>::setInputSource;
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>::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_;
typedef typename Registration<PointSource, PointTarget, Scalar>::Matrix4 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 */
virtual ~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
*/
virtual void
setInputSource (const PointCloudSourceConstPtr &cloud)
{
Registration<PointSource, PointTarget, Scalar>::setInputSource (cloud);
std::vector<pcl::PCLPointField> fields;
pcl::getFields (*cloud, fields);
source_has_normals_ = false;
for (size_t i = 0; i < fields.size (); ++i)
{
if (fields[i].name == "x") x_idx_offset_ = fields[i].offset;
else if (fields[i].name == "y") y_idx_offset_ = fields[i].offset;
else if (fields[i].name == "z") z_idx_offset_ = fields[i].offset;
else if (fields[i].name == "normal_x")
{
source_has_normals_ = true;
nx_idx_offset_ = fields[i].offset;
}
else if (fields[i].name == "normal_y")
{
source_has_normals_ = true;
ny_idx_offset_ = fields[i].offset;
}
else if (fields[i].name == "normal_z")
{
source_has_normals_ = true;
nz_idx_offset_ = fields[i].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
*/
virtual void
setInputTarget (const PointCloudTargetConstPtr &cloud)
{
Registration<PointSource, PointTarget, Scalar>::setInputTarget (cloud);
std::vector<pcl::PCLPointField> fields;
pcl::getFields (*cloud, fields);
target_has_normals_ = false;
for (size_t i = 0; i < fields.size (); ++i)
{
if (fields[i].name == "normal_x" || fields[i].name == "normal_y" || fields[i].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
*/
virtual void
computeTransformation (PointCloudSource &output, const Matrix4 &guess);
/** \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. */
size_t x_idx_offset_, y_idx_offset_, z_idx_offset_;
/** \brief Normal fields offset. */
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.
*
* \author Radu B. Rusu
* \ingroup registration
*/
template <typename PointSource, typename PointTarget, typename Scalar = float>
class IterativeClosestPointWithNormals : public IterativeClosestPoint<PointSource, PointTarget, Scalar>
{
public:
typedef typename IterativeClosestPoint<PointSource, PointTarget, Scalar>::PointCloudSource PointCloudSource;
typedef typename IterativeClosestPoint<PointSource, PointTarget, Scalar>::PointCloudTarget PointCloudTarget;
typedef typename IterativeClosestPoint<PointSource, PointTarget, Scalar>::Matrix4 Matrix4;
using IterativeClosestPoint<PointSource, PointTarget, Scalar>::reg_name_;
using IterativeClosestPoint<PointSource, PointTarget, Scalar>::transformation_estimation_;
using IterativeClosestPoint<PointSource, PointTarget, Scalar>::correspondence_rejectors_;
typedef boost::shared_ptr<IterativeClosestPoint<PointSource, PointTarget, Scalar> > Ptr;
typedef boost::shared_ptr<const IterativeClosestPoint<PointSource, PointTarget, Scalar> > ConstPtr;
/** \brief Empty constructor. */
IterativeClosestPointWithNormals ()
{
reg_name_ = "IterativeClosestPointWithNormals";
transformation_estimation_.reset (new pcl::registration::TransformationEstimationPointToPlaneLLS<PointSource, PointTarget, Scalar> ());
//correspondence_rejectors_.add
};
/** \brief Empty destructor */
virtual ~IterativeClosestPointWithNormals () {}
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);
};
}
#include <pcl/registration/impl/icp.hpp>
#endif //#ifndef PCL_ICP_H_
pcl::IterativeClosestPoint::PointIndicesPtr
PointIndices::Ptr PointIndicesPtr
Definition: icp.h:105
pcl::IterativeClosestPoint::setInputTarget
virtual void setInputTarget(const PointCloudTargetConstPtr &cloud)
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::IterativeClosestPoint::PointCloudTargetPtr
PointCloudTarget::Ptr PointCloudTargetPtr
Definition: icp.h:102
pcl::registration::DefaultConvergenceCriteria
DefaultConvergenceCriteria represents an instantiation of ConvergenceCriteria, and implements the fol...
Definition: default_convergence_criteria.h:65
pcl::IterativeClosestPoint::y_idx_offset_
size_t y_idx_offset_
Definition: icp.h:272
pcl::Registration::transformation_estimation_
TransformationEstimationPtr transformation_estimation_
A TransformationEstimation object, used to calculate the 4x4 rigid transformation.
Definition: registration.h:547
pcl::IterativeClosestPointWithNormals
IterativeClosestPointWithNormals is a special case of IterativeClosestPoint, that uses a transformati...
Definition: icp.h:297
pcl::IterativeClosestPoint::target_has_normals_
bool target_has_normals_
Internal check whether target dataset has normals or not.
Definition: icp.h:283
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:196
pcl::Registration::correspondence_estimation_
CorrespondenceEstimationPtr correspondence_estimation_
A CorrespondenceEstimation object, used to estimate correspondences between the source and the target...
Definition: registration.h:550
pcl
Definition: cloud_iterator.h:46
pcl::IterativeClosestPoint::nz_idx_offset_
size_t nz_idx_offset_
Definition: icp.h:275
pcl::Registration::nr_iterations_
int nr_iterations_
The number of iterations the internal optimization ran for (used internally).
Definition: registration.h:491
pcl::IterativeClosestPoint::getUseReciprocalCorrespondences
bool getUseReciprocalCorrespondences() const
Obtain whether reciprocal correspondence are used or not.
Definition: icp.h:241
pcl::IterativeClosestPoint::PointCloudTargetConstPtr
PointCloudTarget::ConstPtr PointCloudTargetConstPtr
Definition: icp.h:103
pcl::Registration::correspondences_
CorrespondencesPtr correspondences_
The set of correspondences determined at this ICP step.
Definition: registration.h:544
pcl::IterativeClosestPointWithNormals::ConstPtr
boost::shared_ptr< const IterativeClosestPoint< PointSource, PointTarget, Scalar > > ConstPtr
Definition: icp.h:309
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:251
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:58
pcl::Registration::Matrix4
Eigen::Matrix< Scalar, 4, 4 > Matrix4
Definition: registration.h:65
pcl::IterativeClosestPointWithNormals::Ptr
boost::shared_ptr< IterativeClosestPoint< PointSource, PointTarget, Scalar > > Ptr
Definition: icp.h:308
pcl::PointCloud< PointSource >::Ptr
boost::shared_ptr< PointCloud< PointSource > > Ptr
Definition: point_cloud.h:428
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::PointCloudSource
Registration< PointSource, PointTarget, Scalar >::PointCloudSource PointCloudSource
Definition: icp.h:97
pcl::Registration::transformation_
Matrix4 transformation_
The transformation matrix estimated by the registration method.
Definition: registration.h:508
pcl::IterativeClosestPointWithNormals::IterativeClosestPointWithNormals
IterativeClosestPointWithNormals()
Empty constructor.
Definition: icp.h:312
pcl::registration::TransformationEstimationSVD
TransformationEstimationSVD implements SVD-based estimation of the transformation aligning the given ...
Definition: transformation_estimation_svd.h:58
pcl::IterativeClosestPoint::~IterativeClosestPoint
virtual ~IterativeClosestPoint()
Empty destructor.
Definition: icp.h:156
pcl::IterativeClosestPoint::PointCloudSourceConstPtr
PointCloudSource::ConstPtr PointCloudSourceConstPtr
Definition: icp.h:99
pcl::IterativeClosestPoint::Ptr
boost::shared_ptr< IterativeClosestPoint< PointSource, PointTarget, Scalar > > Ptr
Definition: icp.h:108
pcl::IterativeClosestPointWithNormals::~IterativeClosestPointWithNormals
virtual ~IterativeClosestPointWithNormals()
Empty destructor.
Definition: icp.h:320
pcl::PointCloud< PointSource >::ConstPtr
boost::shared_ptr< const PointCloud< PointSource > > ConstPtr
Definition: point_cloud.h:429
pcl::IterativeClosestPoint
IterativeClosestPoint provides a base implementation of the Iterative Closest Point algorithm...
Definition: icp.h:94
pcl::Registration
Registration represents the base registration class for general purpose, ICP-like methods...
Definition: registration.h:62
pcl::PointCloud< PointSource >
pcl::IterativeClosestPoint::source_has_normals_
bool source_has_normals_
Internal check whether source dataset has normals or not.
Definition: icp.h:281
pcl::registration::DefaultConvergenceCriteria::Ptr
boost::shared_ptr< DefaultConvergenceCriteria< Scalar > > Ptr
Definition: default_convergence_criteria.h:68
pcl::IterativeClosestPoint::Matrix4
Registration< PointSource, PointTarget, Scalar >::Matrix4 Matrix4
Definition: icp.h:135
pcl::PointIndices::Ptr
boost::shared_ptr< ::pcl::PointIndices > Ptr
Definition: PointIndices.h:22
pcl::IterativeClosestPoint::x_idx_offset_
size_t x_idx_offset_
XYZ fields offset.
Definition: icp.h:272
pcl::IterativeClosestPoint::computeTransformation
virtual void computeTransformation(PointCloudSource &output, const Matrix4 &guess)
Rigid transformation computation method with initial guess.
Definition: icp.hpp:119
pcl::IterativeClosestPoint::PointCloudTarget
Registration< PointSource, PointTarget, Scalar >::PointCloudTarget PointCloudTarget
Definition: icp.h:101
pcl::IterativeClosestPoint::PointIndicesConstPtr
PointIndices::ConstPtr PointIndicesConstPtr
Definition: icp.h:106
pcl::IterativeClosestPoint::getConvergeCriteria
pcl::registration::DefaultConvergenceCriteria< Scalar >::Ptr getConvergeCriteria()
Returns a pointer to the DefaultConvergenceCriteria used by the IterativeClosestPoint class...
Definition: icp.h:167
pcl::IterativeClosestPoint::convergence_criteria_
pcl::registration::DefaultConvergenceCriteria< Scalar >::Ptr convergence_criteria_
Definition: icp.h:134
pcl::IterativeClosestPoint::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: icp.h:178
pcl::registration::TransformationEstimationPointToPlaneLLS
TransformationEstimationPointToPlaneLLS implements a Linear Least Squares (LLS) approximation for min...
Definition: transformation_estimation_point_to_plane_lls.h:63
pcl::Registration::reg_name_
std::string reg_name_
The registration method name.
Definition: registration.h:482
pcl::IterativeClosestPoint::need_source_blob_
bool need_source_blob_
Checks for whether estimators and rejectors need various data.
Definition: icp.h:286
pcl::IterativeClosestPoint::use_reciprocal_correspondence_
bool use_reciprocal_correspondence_
The correspondence type used for correspondence estimation.
Definition: icp.h:278
pcl::IterativeClosestPoint::setUseReciprocalCorrespondences
void setUseReciprocalCorrespondences(bool use_reciprocal_correspondence)
Set whether to use reciprocal correspondence or not.
Definition: icp.h:234
pcl::IterativeClosestPointWithNormals::PointCloudTarget
IterativeClosestPoint< PointSource, PointTarget, Scalar >::PointCloudTarget PointCloudTarget
Definition: icp.h:301
pcl::IterativeClosestPoint::IterativeClosestPoint
IterativeClosestPoint()
Empty constructor.
Definition: icp.h:138
pcl::registration::CorrespondenceEstimation
CorrespondenceEstimation represents the base class for determining correspondences between target and...
Definition: correspondence_estimation.h:378
pcl::IterativeClosestPoint::z_idx_offset_
size_t z_idx_offset_
Definition: icp.h:272
pcl::IterativeClosestPointWithNormals::PointCloudSource
IterativeClosestPoint< PointSource, PointTarget, Scalar >::PointCloudSource PointCloudSource
Definition: icp.h:300
pcl::IterativeClosestPoint::nx_idx_offset_
size_t nx_idx_offset_
Normal fields offset.
Definition: icp.h:275
pcl::IterativeClosestPointWithNormals::Matrix4
IterativeClosestPoint< PointSource, PointTarget, Scalar >::Matrix4 Matrix4
Definition: icp.h:302
pcl::IterativeClosestPoint::ConstPtr
boost::shared_ptr< const IterativeClosestPoint< PointSource, PointTarget, Scalar > > ConstPtr
Definition: icp.h:109
pcl::IterativeClosestPoint::need_target_blob_
bool need_target_blob_
Definition: icp.h:286
pcl::IterativeClosestPoint::ny_idx_offset_
size_t ny_idx_offset_
Definition: icp.h:275
pcl::getFields
void getFields(const pcl::PointCloud< PointT > &cloud, std::vector< pcl::PCLPointField > &fields)
Get the list of available fields (i.e., dimension/channel)
Definition: io.hpp:79
pcl::IterativeClosestPoint::PointCloudSourcePtr
PointCloudSource::Ptr PointCloudSourcePtr
Definition: icp.h:98
pcl::PointIndices::ConstPtr
boost::shared_ptr< ::pcl::PointIndices const > ConstPtr
Definition: PointIndices.h:23