Point Cloud Library (PCL)  1.12.0-dev
icp.h
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40 
41 #pragma once
42 
43 // PCL includes
44 #include <pcl/registration/correspondence_estimation.h>
45 #include <pcl/registration/default_convergence_criteria.h>
46 #include <pcl/registration/registration.h>
47 #include <pcl/registration/transformation_estimation_point_to_plane_lls.h>
48 #include <pcl/registration/transformation_estimation_svd.h>
49 #include <pcl/registration/transformation_estimation_symmetric_point_to_plane_lls.h>
50 #include <pcl/memory.h> // for dynamic_pointer_cast, pcl::make_shared, shared_ptr
51 
52 namespace pcl {
53 /** \brief @b IterativeClosestPoint provides a base implementation of the Iterative
54  * Closest Point algorithm. The transformation is estimated based on Singular Value
55  * Decomposition (SVD).
56  *
57  * The algorithm has several termination criteria:
58  *
59  * <ol>
60  * <li>Number of iterations has reached the maximum user imposed number of iterations
61  * (via \ref setMaximumIterations)</li> <li>The epsilon (difference) between the
62  * previous transformation and the current estimated transformation is smaller than an
63  * user imposed value (via \ref setTransformationEpsilon)</li> <li>The sum of Euclidean
64  * squared errors is smaller than a user defined threshold (via \ref
65  * setEuclideanFitnessEpsilon)</li>
66  * </ol>
67  *
68  *
69  * Usage example:
70  * \code
71  * IterativeClosestPoint<PointXYZ, PointXYZ> icp;
72  * // Set the input source and target
73  * icp.setInputSource (cloud_source);
74  * icp.setInputTarget (cloud_target);
75  *
76  * // Set the max correspondence distance to 5cm (e.g., correspondences with higher
77  * // distances will be ignored)
78  * icp.setMaxCorrespondenceDistance (0.05);
79  * // Set the maximum number of iterations (criterion 1)
80  * icp.setMaximumIterations (50);
81  * // Set the transformation epsilon (criterion 2)
82  * icp.setTransformationEpsilon (1e-8);
83  * // Set the euclidean distance difference epsilon (criterion 3)
84  * icp.setEuclideanFitnessEpsilon (1);
85  *
86  * // Perform the alignment
87  * icp.align (cloud_source_registered);
88  *
89  * // Obtain the transformation that aligned cloud_source to cloud_source_registered
90  * Eigen::Matrix4f transformation = icp.getFinalTransformation ();
91  * \endcode
92  *
93  * \author Radu B. Rusu, Michael Dixon
94  * \ingroup registration
95  */
96 template <typename PointSource, typename PointTarget, typename Scalar = float>
97 class IterativeClosestPoint : public Registration<PointSource, PointTarget, Scalar> {
98 public:
99  using PointCloudSource =
101  using PointCloudSourcePtr = typename PointCloudSource::Ptr;
102  using PointCloudSourceConstPtr = typename PointCloudSource::ConstPtr;
103 
104  using PointCloudTarget =
106  using PointCloudTargetPtr = typename PointCloudTarget::Ptr;
107  using PointCloudTargetConstPtr = typename PointCloudTarget::ConstPtr;
108 
111 
112  using Ptr = shared_ptr<IterativeClosestPoint<PointSource, PointTarget, Scalar>>;
113  using ConstPtr =
114  shared_ptr<const IterativeClosestPoint<PointSource, PointTarget, Scalar>>;
115 
139 
143 
144  /** \brief Empty constructor. */
146  : x_idx_offset_(0)
147  , y_idx_offset_(0)
148  , z_idx_offset_(0)
149  , nx_idx_offset_(0)
150  , ny_idx_offset_(0)
151  , nz_idx_offset_(0)
153  , source_has_normals_(false)
154  , target_has_normals_(false)
155  {
156  reg_name_ = "IterativeClosestPoint";
158  new pcl::registration::
159  TransformationEstimationSVD<PointSource, PointTarget, Scalar>());
161  new pcl::registration::
163  convergence_criteria_.reset(
166  };
167 
168  /**
169  * \brief Due to `convergence_criteria_` holding references to the class members,
170  * it is tricky to correctly implement its copy and move operations correctly. This
171  * can result in subtle bugs and to prevent them, these operations for ICP have
172  * been disabled.
173  *
174  * \todo: remove deleted ctors and assignments operations after resolving the issue
175  */
179  operator=(const IterativeClosestPoint&) = delete;
181  operator=(IterativeClosestPoint&&) = delete;
182 
183  /** \brief Empty destructor */
185 
186  /** \brief Returns a pointer to the DefaultConvergenceCriteria used by the
187  * IterativeClosestPoint class. This allows to check the convergence state after the
188  * align() method as well as to configure DefaultConvergenceCriteria's parameters not
189  * available through the ICP API before the align() method is called. Please note that
190  * the align method sets max_iterations_, euclidean_fitness_epsilon_ and
191  * transformation_epsilon_ and therefore overrides the default / set values of the
192  * DefaultConvergenceCriteria instance. \return Pointer to the IterativeClosestPoint's
193  * DefaultConvergenceCriteria.
194  */
197  {
198  return convergence_criteria_;
199  }
200 
201  /** \brief Provide a pointer to the input source
202  * (e.g., the point cloud that we want to align to the target)
203  *
204  * \param[in] cloud the input point cloud source
205  */
206  void
208  {
210  const auto fields = pcl::getFields<PointSource>();
211  source_has_normals_ = false;
212  for (const auto& field : fields) {
213  if (field.name == "x")
214  x_idx_offset_ = field.offset;
215  else if (field.name == "y")
216  y_idx_offset_ = field.offset;
217  else if (field.name == "z")
218  z_idx_offset_ = field.offset;
219  else if (field.name == "normal_x") {
220  source_has_normals_ = true;
221  nx_idx_offset_ = field.offset;
222  }
223  else if (field.name == "normal_y") {
224  source_has_normals_ = true;
225  ny_idx_offset_ = field.offset;
226  }
227  else if (field.name == "normal_z") {
228  source_has_normals_ = true;
229  nz_idx_offset_ = field.offset;
230  }
231  }
232  }
233 
234  /** \brief Provide a pointer to the input target
235  * (e.g., the point cloud that we want to align to the target)
236  *
237  * \param[in] cloud the input point cloud target
238  */
239  void
241  {
243  const auto fields = pcl::getFields<PointSource>();
244  target_has_normals_ = false;
245  for (const auto& field : fields) {
246  if (field.name == "normal_x" || field.name == "normal_y" ||
247  field.name == "normal_z") {
248  target_has_normals_ = true;
249  break;
250  }
251  }
252  }
253 
254  /** \brief Set whether to use reciprocal correspondence or not
255  *
256  * \param[in] use_reciprocal_correspondence whether to use reciprocal correspondence
257  * or not
258  */
259  inline void
260  setUseReciprocalCorrespondences(bool use_reciprocal_correspondence)
261  {
262  use_reciprocal_correspondence_ = use_reciprocal_correspondence;
263  }
264 
265  /** \brief Obtain whether reciprocal correspondence are used or not */
266  inline bool
268  {
270  }
271 
272 protected:
273  /** \brief Apply a rigid transform to a given dataset. Here we check whether
274  * the dataset has surface normals in addition to XYZ, and rotate normals as well.
275  * \param[in] input the input point cloud
276  * \param[out] output the resultant output point cloud
277  * \param[in] transform a 4x4 rigid transformation
278  * \note Can be used with cloud_in equal to cloud_out
279  */
280  virtual void
281  transformCloud(const PointCloudSource& input,
282  PointCloudSource& output,
283  const Matrix4& transform);
284 
285  /** \brief Rigid transformation computation method with initial guess.
286  * \param output the transformed input point cloud dataset using the rigid
287  * transformation found \param guess the initial guess of the transformation to
288  * compute
289  */
290  void
291  computeTransformation(PointCloudSource& output, const Matrix4& guess) override;
292 
293  /** \brief Looks at the Estimators and Rejectors and determines whether their
294  * blob-setter methods need to be called */
295  virtual void
297 
298  /** \brief XYZ fields offset. */
300 
301  /** \brief Normal fields offset. */
303 
304  /** \brief The correspondence type used for correspondence estimation. */
306 
307  /** \brief Internal check whether source dataset has normals or not. */
309  /** \brief Internal check whether target dataset has normals or not. */
311 
312  /** \brief Checks for whether estimators and rejectors need various data */
314 };
315 
316 /** \brief @b IterativeClosestPointWithNormals is a special case of
317  * IterativeClosestPoint, that uses a transformation estimated based on
318  * Point to Plane distances by default.
319  *
320  * By default, this implementation uses the traditional point to plane objective
321  * and computes point to plane distances using the normals of the target point
322  * cloud. It also provides the option (through setUseSymmetricObjective) of
323  * using the symmetric objective function of [Rusinkiewicz 2019]. This objective
324  * uses the normals of both the source and target point cloud and has a similar
325  * computational cost to the traditional point to plane objective while also
326  * offering improved convergence speed and a wider basin of convergence.
327  *
328  * Note that this implementation not demean the point clouds which can lead
329  * to increased numerical error. If desired, a user can demean the point cloud,
330  * run iterative closest point, and composite the resulting ICP transformation
331  * with the translations from demeaning to obtain a transformation between
332  * the original point clouds.
333  *
334  * \author Radu B. Rusu, Matthew Cong
335  * \ingroup registration
336  */
337 template <typename PointSource, typename PointTarget, typename Scalar = float>
339 : public IterativeClosestPoint<PointSource, PointTarget, Scalar> {
340 public:
341  using PointCloudSource = typename IterativeClosestPoint<PointSource,
342  PointTarget,
344  using PointCloudTarget = typename IterativeClosestPoint<PointSource,
345  PointTarget,
347  using Matrix4 =
349 
355 
356  using Ptr = shared_ptr<IterativeClosestPoint<PointSource, PointTarget, Scalar>>;
357  using ConstPtr =
358  shared_ptr<const IterativeClosestPoint<PointSource, PointTarget, Scalar>>;
359 
360  /** \brief Empty constructor. */
362  {
363  reg_name_ = "IterativeClosestPointWithNormals";
366  // correspondence_rejectors_.add
367  };
368 
369  /** \brief Empty destructor */
371 
372  /** \brief Set whether to use a symmetric objective function or not
373  *
374  * \param[in] use_symmetric_objective whether to use a symmetric objective function or
375  * not
376  */
377  inline void
378  setUseSymmetricObjective(bool use_symmetric_objective)
379  {
380  use_symmetric_objective_ = use_symmetric_objective;
382  auto symmetric_transformation_estimation = pcl::make_shared<
384  PointSource,
385  PointTarget,
386  Scalar>>();
387  symmetric_transformation_estimation->setEnforceSameDirectionNormals(
389  transformation_estimation_ = symmetric_transformation_estimation;
390  }
391  else {
394  PointTarget,
395  Scalar>());
396  }
397  }
398 
399  /** \brief Obtain whether a symmetric objective is used or not */
400  inline bool
402  {
404  }
405 
406  /** \brief Set whether or not to negate source or target normals on a per-point basis
407  * such that they point in the same direction. Only applicable to the symmetric
408  * objective function.
409  *
410  * \param[in] enforce_same_direction_normals whether to negate source or target
411  * normals on a per-point basis such that they point in the same direction.
412  */
413  inline void
414  setEnforceSameDirectionNormals(bool enforce_same_direction_normals)
415  {
416  enforce_same_direction_normals_ = enforce_same_direction_normals;
417  auto symmetric_transformation_estimation = dynamic_pointer_cast<
419  PointTarget,
420  Scalar>>(
422  if (symmetric_transformation_estimation)
423  symmetric_transformation_estimation->setEnforceSameDirectionNormals(
425  }
426 
427  /** \brief Obtain whether source or target normals are negated on a per-point basis
428  * such that they point in the same direction or not */
429  inline bool
431  {
433  }
434 
435 protected:
436  /** \brief Apply a rigid transform to a given dataset
437  * \param[in] input the input point cloud
438  * \param[out] output the resultant output point cloud
439  * \param[in] transform a 4x4 rigid transformation
440  * \note Can be used with cloud_in equal to cloud_out
441  */
442  virtual void
443  transformCloud(const PointCloudSource& input,
444  PointCloudSource& output,
445  const Matrix4& transform);
446 
447  /** \brief Type of objective function (asymmetric vs. symmetric) used for transform
448  * estimation */
450  /** \brief Whether or not to negate source and/or target normals such that they point
451  * in the same direction in the symmetric objective function */
453 };
454 
455 } // namespace pcl
456 
457 #include <pcl/registration/impl/icp.hpp>
pcl::IterativeClosestPoint< PointSource, PointTarget >::Ptr
shared_ptr< IterativeClosestPoint< PointSource, PointTarget, float > > Ptr
Definition: icp.h:112
pcl::IterativeClosestPointWithNormals::use_symmetric_objective_
bool use_symmetric_objective_
Type of objective function (asymmetric vs.
Definition: icp.h:449
pcl::IterativeClosestPoint::getConvergeCriteria
pcl::registration::DefaultConvergenceCriteria< Scalar >::Ptr getConvergeCriteria()
Returns a pointer to the DefaultConvergenceCriteria used by the IterativeClosestPoint class.
Definition: icp.h:196
pcl::Registration< PointSource, PointTarget, float >::correspondences_
CorrespondencesPtr correspondences_
The set of correspondences determined at this ICP step.
Definition: registration.h:631
pcl
Definition: convolution.h:46
pcl::IterativeClosestPoint::setUseReciprocalCorrespondences
void setUseReciprocalCorrespondences(bool use_reciprocal_correspondence)
Set whether to use reciprocal correspondence or not.
Definition: icp.h:260
pcl::make_shared
shared_ptr< T > make_shared(Args &&... args)
Returns a pcl::shared_ptr compliant with type T's allocation policy.
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.hpp:47
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:61
pcl::Registration
Registration represents the base registration class for general purpose, ICP-like methods.
Definition: registration.h:57
pcl::IterativeClosestPoint::y_idx_offset_
std::size_t y_idx_offset_
Definition: icp.h:299
pcl::registration::DefaultConvergenceCriteria
DefaultConvergenceCriteria represents an instantiation of ConvergenceCriteria, and implements the fol...
Definition: default_convergence_criteria.h:65
pcl::IterativeClosestPoint::operator=
IterativeClosestPoint & operator=(const IterativeClosestPoint &)=delete
pcl::IterativeClosestPoint::need_target_blob_
bool need_target_blob_
Definition: icp.h:313
pcl::Registration< PointSource, PointTarget, float >::transformation_rotation_epsilon_
double transformation_rotation_epsilon_
The maximum rotation difference between two consecutive transformations in order to consider converge...
Definition: registration.h:600
pcl::IterativeClosestPoint< PointSource, PointTarget >::PointCloudSourcePtr
typename PointCloudSource::Ptr PointCloudSourcePtr
Definition: icp.h:101
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:309
pcl::IterativeClosestPointWithNormals::IterativeClosestPointWithNormals
IterativeClosestPointWithNormals()
Empty constructor.
Definition: icp.h:361
pcl::IterativeClosestPoint
IterativeClosestPoint provides a base implementation of the Iterative Closest Point algorithm.
Definition: icp.h:97
pcl::IterativeClosestPointWithNormals::setUseSymmetricObjective
void setUseSymmetricObjective(bool use_symmetric_objective)
Set whether to use a symmetric objective function or not.
Definition: icp.h:378
pcl::Registration< PointSource, PointTarget, float >::transformation_
Matrix4 transformation_
The transformation matrix estimated by the registration method.
Definition: registration.h:586
pcl::IterativeClosestPoint::use_reciprocal_correspondence_
bool use_reciprocal_correspondence_
The correspondence type used for correspondence estimation.
Definition: icp.h:305
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:452
pcl::IterativeClosestPoint< PointSource, PointTarget >::PointCloudTarget
typename Registration< PointSource, PointTarget, float >::PointCloudTarget PointCloudTarget
Definition: icp.h:105
pcl::registration::CorrespondenceEstimationBase< PointSource, PointTarget, float >
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:207
pcl::IterativeClosestPointWithNormals::getUseSymmetricObjective
bool getUseSymmetricObjective() const
Obtain whether a symmetric objective is used or not.
Definition: icp.h:401
pcl::PointCloud< PointSource >
pcl::Registration::Matrix4
Eigen::Matrix< Scalar, 4, 4 > Matrix4
Definition: registration.h:59
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:267
pcl::IterativeClosestPoint::PointCloudSource
typename Registration< PointSource, PointTarget, Scalar >::PointCloudSource PointCloudSource
Definition: icp.h:100
pcl::registration::DefaultConvergenceCriteria::Ptr
shared_ptr< DefaultConvergenceCriteria< Scalar > > Ptr
Definition: default_convergence_criteria.h:67
pcl::IterativeClosestPointWithNormals::~IterativeClosestPointWithNormals
virtual ~IterativeClosestPointWithNormals()
Empty destructor.
Definition: icp.h:370
pcl::IterativeClosestPoint< PointSource, PointTarget >::PointCloudTargetConstPtr
typename PointCloudTarget::ConstPtr PointCloudTargetConstPtr
Definition: icp.h:107
pcl::IterativeClosestPoint::nx_idx_offset_
std::size_t nx_idx_offset_
Normal fields offset.
Definition: icp.h:302
pcl::PointIndices::ConstPtr
shared_ptr< const ::pcl::PointIndices > ConstPtr
Definition: PointIndices.h:14
pcl::IterativeClosestPointWithNormals::Ptr
shared_ptr< IterativeClosestPoint< PointSource, PointTarget, Scalar > > Ptr
Definition: icp.h:356
pcl::IterativeClosestPointWithNormals
IterativeClosestPointWithNormals is a special case of IterativeClosestPoint, that uses a transformati...
Definition: icp.h:338
pcl::IterativeClosestPointWithNormals::ConstPtr
shared_ptr< const IterativeClosestPoint< PointSource, PointTarget, Scalar > > ConstPtr
Definition: icp.h:358
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:240
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:430
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:414
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:50
pcl::IterativeClosestPoint::convergence_criteria_
pcl::registration::DefaultConvergenceCriteria< Scalar >::Ptr convergence_criteria_
Definition: icp.h:141
pcl::IterativeClosestPoint< PointSource, PointTarget >::PointCloudSourceConstPtr
typename PointCloudSource::ConstPtr PointCloudSourceConstPtr
Definition: icp.h:102
pcl::IterativeClosestPoint::ny_idx_offset_
std::size_t ny_idx_offset_
Definition: icp.h:302
pcl::Registration< PointSource, PointTarget, float >::correspondence_rejectors_
std::vector< CorrespondenceRejectorPtr > correspondence_rejectors_
The list of correspondence rejectors to use.
Definition: registration.h:642
pcl::registration::TransformationEstimationSymmetricPointToPlaneLLS
TransformationEstimationSymmetricPointToPlaneLLS implements a Linear Least Squares (LLS) approximatio...
Definition: transformation_estimation_symmetric_point_to_plane_lls.h:59
pcl::IterativeClosestPoint::IterativeClosestPoint
IterativeClosestPoint()
Empty constructor.
Definition: icp.h:145
pcl::registration
Definition: convergence_criteria.h:46
pcl::PointIndices::Ptr
shared_ptr< ::pcl::PointIndices > Ptr
Definition: PointIndices.h:13
pcl::IterativeClosestPoint::z_idx_offset_
std::size_t z_idx_offset_
Definition: icp.h:299
pcl::Registration< PointSource, PointTarget, float >::nr_iterations_
int nr_iterations_
The number of iterations the internal optimization ran for (used internally).
Definition: registration.h:568
pcl::IterativeClosestPoint::nz_idx_offset_
std::size_t nz_idx_offset_
Definition: icp.h:302
pcl::IterativeClosestPoint::computeTransformation
void computeTransformation(PointCloudSource &output, const Matrix4 &guess) override
Rigid transformation computation method with initial guess.
Definition: icp.hpp:114
pcl::IterativeClosestPoint::need_source_blob_
bool need_source_blob_
Checks for whether estimators and rejectors need various data.
Definition: icp.h:313
pcl::IterativeClosestPointWithNormals::PointCloudSource
typename IterativeClosestPoint< PointSource, PointTarget, Scalar >::PointCloudSource PointCloudSource
Definition: icp.h:343
pcl::IterativeClosestPoint::~IterativeClosestPoint
~IterativeClosestPoint()
Empty destructor.
Definition: icp.h:184
pcl::IterativeClosestPointWithNormals::Matrix4
typename IterativeClosestPoint< PointSource, PointTarget, Scalar >::Matrix4 Matrix4
Definition: icp.h:348
pcl::PCLBase< PointSource >::PointIndicesPtr
PointIndices::Ptr PointIndicesPtr
Definition: pcl_base.h:76
pcl::IterativeClosestPoint< PointSource, PointTarget >::PointCloudTargetPtr
typename PointCloudTarget::Ptr PointCloudTargetPtr
Definition: icp.h:106
pcl::Registration< PointSource, PointTarget, float >::transformation_estimation_
TransformationEstimationPtr transformation_estimation_
A TransformationEstimation object, used to calculate the 4x4 rigid transformation.
Definition: registration.h:635
pcl::IterativeClosestPoint::source_has_normals_
bool source_has_normals_
Internal check whether source dataset has normals or not.
Definition: icp.h:308
pcl::IterativeClosestPoint::target_has_normals_
bool target_has_normals_
Internal check whether target dataset has normals or not.
Definition: icp.h:310
pcl::IterativeClosestPoint< PointSource, PointTarget >::Matrix4
typename Registration< PointSource, PointTarget, float >::Matrix4 Matrix4
Definition: icp.h:142
pcl::IterativeClosestPoint< PointSource, PointTarget >::ConstPtr
shared_ptr< const IterativeClosestPoint< PointSource, PointTarget, float > > ConstPtr
Definition: icp.h:114
pcl::IterativeClosestPoint::getUseReciprocalCorrespondences
bool getUseReciprocalCorrespondences() const
Obtain whether reciprocal correspondence are used or not.
Definition: icp.h:267
memory.h
Defines functions, macros and traits for allocating and using memory.
pcl::Registration< PointSource, PointTarget, float >::reg_name_
std::string reg_name_
The registration method name.
Definition: registration.h:558
pcl::registration::TransformationEstimationPointToPlaneLLS
TransformationEstimationPointToPlaneLLS implements a Linear Least Squares (LLS) approximation for min...
Definition: transformation_estimation_point_to_plane_lls.h:63
pcl::IterativeClosestPointWithNormals::PointCloudTarget
typename IterativeClosestPoint< PointSource, PointTarget, Scalar >::PointCloudTarget PointCloudTarget
Definition: icp.h:346
pcl::Registration< PointSource, PointTarget, float >::correspondence_estimation_
CorrespondenceEstimationPtr correspondence_estimation_
A CorrespondenceEstimation object, used to estimate correspondences between the source and the target...
Definition: registration.h:639
pcl::IterativeClosestPoint::x_idx_offset_
std::size_t x_idx_offset_
XYZ fields offset.
Definition: icp.h:299