Point Cloud Library (PCL)  1.11.1-dev
icp.h
1 /*
2  * Software License Agreement (BSD License)
3  *
4  * Point Cloud Library (PCL) - www.pointclouds.org
5  * Copyright (c) 2010, Willow Garage, Inc.
6  * Copyright (c) 2012-, Open Perception, Inc.
7  *
8  * All rights reserved.
9  *
10  * Redistribution and use in source and binary forms, with or without
11  * modification, are permitted provided that the following conditions
12  * are met:
13  *
14  * * Redistributions of source code must retain the above copyright
15  * notice, this list of conditions and the following disclaimer.
16  * * Redistributions in binary form must reproduce the above
17  * copyright notice, this list of conditions and the following
18  * disclaimer in the documentation and/or other materials provided
19  * with the distribution.
20  * * Neither the name of the copyright holder(s) nor the names of its
21  * contributors may be used to endorse or promote products derived
22  * from this software without specific prior written permission.
23  *
24  * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
25  * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
26  * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
27  * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
28  * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
29  * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
30  * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
31  * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
32  * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
33  * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
34  * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
35  * POSSIBILITY OF SUCH DAMAGE.
36  *
37  * $Id$
38  *
39  */
40 
41 #pragma once
42 
43 // PCL includes
44 #include <pcl/memory.h> // for dynamic_pointer_cast, pcl::make_shared, shared_ptr
45 #include <pcl/registration/registration.h>
46 #include <pcl/registration/transformation_estimation_svd.h>
47 #include <pcl/registration/transformation_estimation_point_to_plane_lls.h>
48 #include <pcl/registration/transformation_estimation_symmetric_point_to_plane_lls.h>
49 #include <pcl/registration/correspondence_estimation.h>
50 #include <pcl/registration/default_convergence_criteria.h>
51 
52 
53 namespace pcl
54 {
55  /** \brief @b IterativeClosestPoint provides a base implementation of the Iterative Closest Point algorithm.
56  * The transformation is estimated based on Singular Value Decomposition (SVD).
57  *
58  * The algorithm has several termination criteria:
59  *
60  * <ol>
61  * <li>Number of iterations has reached the maximum user imposed number of iterations (via \ref setMaximumIterations)</li>
62  * <li>The epsilon (difference) between the previous transformation and the current estimated transformation is smaller than an user imposed value (via \ref setTransformationEpsilon)</li>
63  * <li>The sum of Euclidean squared errors is smaller than a user defined threshold (via \ref setEuclideanFitnessEpsilon)</li>
64  * </ol>
65  *
66  *
67  * Usage example:
68  * \code
69  * IterativeClosestPoint<PointXYZ, PointXYZ> icp;
70  * // Set the input source and target
71  * icp.setInputCloud (cloud_source);
72  * icp.setInputTarget (cloud_target);
73  *
74  * // Set the max correspondence distance to 5cm (e.g., correspondences with higher distances will be ignored)
75  * icp.setMaxCorrespondenceDistance (0.05);
76  * // Set the maximum number of iterations (criterion 1)
77  * icp.setMaximumIterations (50);
78  * // Set the transformation epsilon (criterion 2)
79  * icp.setTransformationEpsilon (1e-8);
80  * // Set the euclidean distance difference epsilon (criterion 3)
81  * icp.setEuclideanFitnessEpsilon (1);
82  *
83  * // Perform the alignment
84  * icp.align (cloud_source_registered);
85  *
86  * // Obtain the transformation that aligned cloud_source to cloud_source_registered
87  * Eigen::Matrix4f transformation = icp.getFinalTransformation ();
88  * \endcode
89  *
90  * \author Radu B. Rusu, Michael Dixon
91  * \ingroup registration
92  */
93  template <typename PointSource, typename PointTarget, typename Scalar = float>
94  class IterativeClosestPoint : public Registration<PointSource, PointTarget, Scalar>
95  {
96  public:
98  using PointCloudSourcePtr = typename PointCloudSource::Ptr;
99  using PointCloudSourceConstPtr = typename PointCloudSource::ConstPtr;
100 
102  using PointCloudTargetPtr = typename PointCloudTarget::Ptr;
103  using PointCloudTargetConstPtr = typename PointCloudTarget::ConstPtr;
104 
107 
108  using Ptr = shared_ptr<IterativeClosestPoint<PointSource, PointTarget, Scalar> >;
109  using ConstPtr = shared_ptr<const IterativeClosestPoint<PointSource, PointTarget, Scalar> >;
110 
133 
136 
137  /** \brief Empty constructor. */
139  : x_idx_offset_ (0)
140  , y_idx_offset_ (0)
141  , z_idx_offset_ (0)
142  , nx_idx_offset_ (0)
143  , ny_idx_offset_ (0)
144  , nz_idx_offset_ (0)
146  , source_has_normals_ (false)
147  , target_has_normals_ (false)
148  {
149  reg_name_ = "IterativeClosestPoint";
153  };
154 
155  /**
156  * \brief Due to `convergence_criteria_` holding references to the class members,
157  * it is tricky to correctly implement its copy and move operations correctly. This
158  * can result in subtle bugs and to prevent them, these operations for ICP have
159  * been disabled.
160  *
161  * \todo: remove deleted ctors and assignments operations after resolving the issue
162  */
167 
168  /** \brief Empty destructor */
170 
171  /** \brief Returns a pointer to the DefaultConvergenceCriteria used by the IterativeClosestPoint class.
172  * This allows to check the convergence state after the align() method as well as to configure
173  * DefaultConvergenceCriteria's parameters not available through the ICP API before the align()
174  * method is called. Please note that the align method sets max_iterations_,
175  * euclidean_fitness_epsilon_ and transformation_epsilon_ and therefore overrides the default / set
176  * values of the DefaultConvergenceCriteria instance.
177  * \return Pointer to the IterativeClosestPoint's DefaultConvergenceCriteria.
178  */
181  {
182  return convergence_criteria_;
183  }
184 
185  /** \brief Provide a pointer to the input source
186  * (e.g., the point cloud that we want to align to the target)
187  *
188  * \param[in] cloud the input point cloud source
189  */
190  void
191  setInputSource (const PointCloudSourceConstPtr &cloud) override
192  {
194  const auto fields = pcl::getFields<PointSource> ();
195  source_has_normals_ = false;
196  for (const auto &field : fields)
197  {
198  if (field.name == "x") x_idx_offset_ = field.offset;
199  else if (field.name == "y") y_idx_offset_ = field.offset;
200  else if (field.name == "z") z_idx_offset_ = field.offset;
201  else if (field.name == "normal_x")
202  {
203  source_has_normals_ = true;
204  nx_idx_offset_ = field.offset;
205  }
206  else if (field.name == "normal_y")
207  {
208  source_has_normals_ = true;
209  ny_idx_offset_ = field.offset;
210  }
211  else if (field.name == "normal_z")
212  {
213  source_has_normals_ = true;
214  nz_idx_offset_ = field.offset;
215  }
216  }
217  }
218 
219  /** \brief Provide a pointer to the input target
220  * (e.g., the point cloud that we want to align to the target)
221  *
222  * \param[in] cloud the input point cloud target
223  */
224  void
225  setInputTarget (const PointCloudTargetConstPtr &cloud) override
226  {
228  const auto fields = pcl::getFields<PointSource> ();
229  target_has_normals_ = false;
230  for (const auto &field : fields)
231  {
232  if (field.name == "normal_x" || field.name == "normal_y" || field.name == "normal_z")
233  {
234  target_has_normals_ = true;
235  break;
236  }
237  }
238  }
239 
240  /** \brief Set whether to use reciprocal correspondence or not
241  *
242  * \param[in] use_reciprocal_correspondence whether to use reciprocal correspondence or not
243  */
244  inline void
245  setUseReciprocalCorrespondences (bool use_reciprocal_correspondence)
246  {
247  use_reciprocal_correspondence_ = use_reciprocal_correspondence;
248  }
249 
250  /** \brief Obtain whether reciprocal correspondence are used or not */
251  inline bool
253  {
255  }
256 
257  protected:
258 
259  /** \brief Apply a rigid transform to a given dataset. Here we check whether whether
260  * the dataset has surface normals in addition to XYZ, and rotate normals as well.
261  * \param[in] input the input point cloud
262  * \param[out] output the resultant output point cloud
263  * \param[in] transform a 4x4 rigid transformation
264  * \note Can be used with cloud_in equal to cloud_out
265  */
266  virtual void
267  transformCloud (const PointCloudSource &input,
268  PointCloudSource &output,
269  const Matrix4 &transform);
270 
271  /** \brief Rigid transformation computation method with initial guess.
272  * \param output the transformed input point cloud dataset using the rigid transformation found
273  * \param guess the initial guess of the transformation to compute
274  */
275  void
276  computeTransformation (PointCloudSource &output, const Matrix4 &guess) override;
277 
278  /** \brief Looks at the Estimators and Rejectors and determines whether their blob-setter methods need to be called */
279  virtual void
281 
282  /** \brief XYZ fields offset. */
284 
285  /** \brief Normal fields offset. */
287 
288  /** \brief The correspondence type used for correspondence estimation. */
290 
291  /** \brief Internal check whether source dataset has normals or not. */
293  /** \brief Internal check whether target dataset has normals or not. */
295 
296  /** \brief Checks for whether estimators and rejectors need various data */
298  };
299 
300  /** \brief @b IterativeClosestPointWithNormals is a special case of
301  * IterativeClosestPoint, that uses a transformation estimated based on
302  * Point to Plane distances by default.
303  *
304  * By default, this implementation uses the traditional point to plane objective
305  * and computes point to plane distances using the normals of the target point
306  * cloud. It also provides the option (through setUseSymmetricObjective) of
307  * using the symmetric objective function of [Rusinkiewicz 2019]. This objective
308  * uses the normals of both the source and target point cloud and has a similar
309  * computational cost to the traditional point to plane objective while also
310  * offering improved convergence speed and a wider basin of convergence.
311  *
312  * Note that this implementation not demean the point clouds which can lead
313  * to increased numerical error. If desired, a user can demean the point cloud,
314  * run iterative closest point, and composite the resulting ICP transformation
315  * with the translations from demeaning to obtain a transformation between
316  * the original point clouds.
317  *
318  * \author Radu B. Rusu, Matthew Cong
319  * \ingroup registration
320  */
321  template <typename PointSource, typename PointTarget, typename Scalar = float>
322  class IterativeClosestPointWithNormals : public IterativeClosestPoint<PointSource, PointTarget, Scalar>
323  {
324  public:
328 
332 
333  using Ptr = shared_ptr<IterativeClosestPoint<PointSource, PointTarget, Scalar> >;
334  using ConstPtr = shared_ptr<const IterativeClosestPoint<PointSource, PointTarget, Scalar> >;
335 
336  /** \brief Empty constructor. */
338  {
339  reg_name_ = "IterativeClosestPointWithNormals";
340  setUseSymmetricObjective (false);
342  //correspondence_rejectors_.add
343  };
344 
345  /** \brief Empty destructor */
347 
348  /** \brief Set whether to use a symmetric objective function or not
349  *
350  * \param[in] use_symmetric_objective whether to use a symmetric objective function or not
351  */
352  inline void
353  setUseSymmetricObjective (bool use_symmetric_objective)
354  {
355  use_symmetric_objective_ = use_symmetric_objective;
357  {
358  auto symmetric_transformation_estimation = pcl::make_shared<pcl::registration::TransformationEstimationSymmetricPointToPlaneLLS<PointSource, PointTarget, Scalar> > ();
359  symmetric_transformation_estimation->setEnforceSameDirectionNormals (enforce_same_direction_normals_);
360  transformation_estimation_ = symmetric_transformation_estimation;
361  }
362  else
363  {
365  }
366  }
367 
368  /** \brief Obtain whether a symmetric objective is used or not */
369  inline bool
371  {
373  }
374 
375  /** \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.
376  *
377  * \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.
378  */
379  inline void
380  setEnforceSameDirectionNormals (bool enforce_same_direction_normals)
381  {
382  enforce_same_direction_normals_ = enforce_same_direction_normals;
383  auto symmetric_transformation_estimation = dynamic_pointer_cast<pcl::registration::TransformationEstimationSymmetricPointToPlaneLLS<PointSource, PointTarget, Scalar> >(transformation_estimation_);
384  if (symmetric_transformation_estimation)
385  symmetric_transformation_estimation->setEnforceSameDirectionNormals (enforce_same_direction_normals_);
386  }
387 
388  /** \brief Obtain whether source or target normals are negated on a per-point basis such that they point in the same direction or not */
389  inline bool
391  {
393  }
394 
395  protected:
396 
397  /** \brief Apply a rigid transform to a given dataset
398  * \param[in] input the input point cloud
399  * \param[out] output the resultant output point cloud
400  * \param[in] transform a 4x4 rigid transformation
401  * \note Can be used with cloud_in equal to cloud_out
402  */
403  virtual void
404  transformCloud (const PointCloudSource &input,
405  PointCloudSource &output,
406  const Matrix4 &transform);
407 
408  /** \brief Type of objective function (asymmetric vs. symmetric) used for transform estimation */
410  /** \brief Whether or not to negate source and/or target normals such that they point in the same direction in the symmetric objective function */
412  };
413 
414 }
415 
416 #include <pcl/registration/impl/icp.hpp>
pcl::IterativeClosestPoint< PointSource, PointTarget >::Ptr
shared_ptr< IterativeClosestPoint< PointSource, PointTarget, float > > Ptr
Definition: icp.h:108
pcl::IterativeClosestPointWithNormals::use_symmetric_objective_
bool use_symmetric_objective_
Type of objective function (asymmetric vs.
Definition: icp.h:409
pcl::IterativeClosestPoint::getConvergeCriteria
pcl::registration::DefaultConvergenceCriteria< Scalar >::Ptr getConvergeCriteria()
Returns a pointer to the DefaultConvergenceCriteria used by the IterativeClosestPoint class.
Definition: icp.h:180
pcl::Registration< PointSource, PointTarget, float >::correspondences_
CorrespondencesPtr correspondences_
The set of correspondences determined at this ICP step.
Definition: registration.h:556
pcl
Definition: convolution.h:46
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:245
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:47
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:283
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::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:297
pcl::IterativeClosestPoint< PointSource, PointTarget >::PointCloudSourcePtr
typename PointCloudSource::Ptr PointCloudSourcePtr
Definition: icp.h:98
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:293
pcl::IterativeClosestPointWithNormals::IterativeClosestPointWithNormals
IterativeClosestPointWithNormals()
Empty constructor.
Definition: icp.h:337
pcl::IterativeClosestPoint
IterativeClosestPoint provides a base implementation of the Iterative Closest Point algorithm.
Definition: icp.h:94
pcl::IterativeClosestPointWithNormals::setUseSymmetricObjective
void setUseSymmetricObjective(bool use_symmetric_objective)
Set whether to use a symmetric objective function or not.
Definition: icp.h:353
pcl::Registration< PointSource, PointTarget, float >::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:289
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:411
pcl::IterativeClosestPoint< PointSource, PointTarget >::PointCloudTarget
typename Registration< PointSource, PointTarget, float >::PointCloudTarget PointCloudTarget
Definition: icp.h:101
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:191
pcl::IterativeClosestPointWithNormals::getUseSymmetricObjective
bool getUseSymmetricObjective() const
Obtain whether a symmetric objective is used or not.
Definition: icp.h:370
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:256
pcl::IterativeClosestPoint< PointSource, PointTarget >::PointCloudSource
typename Registration< PointSource, PointTarget, float >::PointCloudSource PointCloudSource
Definition: icp.h:97
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:346
pcl::IterativeClosestPoint< PointSource, PointTarget >::PointCloudTargetConstPtr
typename PointCloudTarget::ConstPtr PointCloudTargetConstPtr
Definition: icp.h:103
pcl::IterativeClosestPoint::nx_idx_offset_
std::size_t nx_idx_offset_
Normal fields offset.
Definition: icp.h:286
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:333
pcl::IterativeClosestPointWithNormals
IterativeClosestPointWithNormals is a special case of IterativeClosestPoint, that uses a transformati...
Definition: icp.h:322
pcl::IterativeClosestPointWithNormals::ConstPtr
shared_ptr< const IterativeClosestPoint< PointSource, PointTarget, Scalar > > ConstPtr
Definition: icp.h:334
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:225
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:390
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:380
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:51
pcl::IterativeClosestPoint::convergence_criteria_
pcl::registration::DefaultConvergenceCriteria< Scalar >::Ptr convergence_criteria_
Definition: icp.h:134
pcl::IterativeClosestPoint< PointSource, PointTarget >::PointCloudSourceConstPtr
typename PointCloudSource::ConstPtr PointCloudSourceConstPtr
Definition: icp.h:99
pcl::IterativeClosestPoint::ny_idx_offset_
std::size_t ny_idx_offset_
Definition: icp.h:286
pcl::registration::CorrespondenceEstimation
CorrespondenceEstimation represents the base class for determining correspondences between target and...
Definition: correspondence_estimation.h:362
pcl::IterativeClosestPoint::IterativeClosestPoint
IterativeClosestPoint()
Empty constructor.
Definition: icp.h:138
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:283
pcl::Registration< PointSource, PointTarget, float >::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:286
pcl::IterativeClosestPoint::computeTransformation
void computeTransformation(PointCloudSource &output, const Matrix4 &guess) override
Rigid transformation computation method with initial guess.
Definition: icp.hpp:120
pcl::IterativeClosestPoint::need_source_blob_
bool need_source_blob_
Checks for whether estimators and rejectors need various data.
Definition: icp.h:297
pcl::IterativeClosestPointWithNormals::PointCloudSource
typename IterativeClosestPoint< PointSource, PointTarget, Scalar >::PointCloudSource PointCloudSource
Definition: icp.h:325
pcl::IterativeClosestPoint::~IterativeClosestPoint
~IterativeClosestPoint()
Empty destructor.
Definition: icp.h:169
pcl::IterativeClosestPointWithNormals::Matrix4
typename IterativeClosestPoint< PointSource, PointTarget, Scalar >::Matrix4 Matrix4
Definition: icp.h:327
pcl::PCLBase< PointSource >::PointIndicesPtr
PointIndices::Ptr PointIndicesPtr
Definition: pcl_base.h:76
pcl::IterativeClosestPoint< PointSource, PointTarget >::PointCloudTargetPtr
typename PointCloudTarget::Ptr PointCloudTargetPtr
Definition: icp.h:102
pcl::Registration< PointSource, PointTarget, float >::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:292
pcl::IterativeClosestPoint::target_has_normals_
bool target_has_normals_
Internal check whether target dataset has normals or not.
Definition: icp.h:294
pcl::IterativeClosestPoint< PointSource, PointTarget >::Matrix4
typename Registration< PointSource, PointTarget, float >::Matrix4 Matrix4
Definition: icp.h:135
pcl::IterativeClosestPoint< PointSource, PointTarget >::ConstPtr
shared_ptr< const IterativeClosestPoint< PointSource, PointTarget, float > > ConstPtr
Definition: icp.h:109
pcl::IterativeClosestPoint::getUseReciprocalCorrespondences
bool getUseReciprocalCorrespondences() const
Obtain whether reciprocal correspondence are used or not.
Definition: icp.h:252
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:489
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:326
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:562
pcl::IterativeClosestPoint::x_idx_offset_
std::size_t x_idx_offset_
XYZ fields offset.
Definition: icp.h:283