Point Cloud Library (PCL)  1.11.0
icp.h
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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/sample_consensus/ransac.h>
46 #include <pcl/sample_consensus/sac_model_registration.h>
47 #include <pcl/registration/registration.h>
48 #include <pcl/registration/transformation_estimation_svd.h>
49 #include <pcl/registration/transformation_estimation_point_to_plane_lls.h>
50 #include <pcl/registration/transformation_estimation_symmetric_point_to_plane_lls.h>
51 #include <pcl/registration/correspondence_estimation.h>
52 #include <pcl/registration/default_convergence_criteria.h>
53 
54 
55 namespace pcl
56 {
57  /** \brief @b IterativeClosestPoint provides a base implementation of the Iterative Closest Point algorithm.
58  * The transformation is estimated based on Singular Value Decomposition (SVD).
59  *
60  * The algorithm has several termination criteria:
61  *
62  * <ol>
63  * <li>Number of iterations has reached the maximum user imposed number of iterations (via \ref setMaximumIterations)</li>
64  * <li>The epsilon (difference) between the previous transformation and the current estimated transformation is smaller than an user imposed value (via \ref setTransformationEpsilon)</li>
65  * <li>The sum of Euclidean squared errors is smaller than a user defined threshold (via \ref 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.setInputCloud (cloud_source);
74  * icp.setInputTarget (cloud_target);
75  *
76  * // Set the max correspondence distance to 5cm (e.g., correspondences with higher distances will be ignored)
77  * icp.setMaxCorrespondenceDistance (0.05);
78  * // Set the maximum number of iterations (criterion 1)
79  * icp.setMaximumIterations (50);
80  * // Set the transformation epsilon (criterion 2)
81  * icp.setTransformationEpsilon (1e-8);
82  * // Set the euclidean distance difference epsilon (criterion 3)
83  * icp.setEuclideanFitnessEpsilon (1);
84  *
85  * // Perform the alignment
86  * icp.align (cloud_source_registered);
87  *
88  * // Obtain the transformation that aligned cloud_source to cloud_source_registered
89  * Eigen::Matrix4f transformation = icp.getFinalTransformation ();
90  * \endcode
91  *
92  * \author Radu B. Rusu, Michael Dixon
93  * \ingroup registration
94  */
95  template <typename PointSource, typename PointTarget, typename Scalar = float>
96  class IterativeClosestPoint : public Registration<PointSource, PointTarget, Scalar>
97  {
98  public:
100  using PointCloudSourcePtr = typename PointCloudSource::Ptr;
101  using PointCloudSourceConstPtr = typename PointCloudSource::ConstPtr;
102 
104  using PointCloudTargetPtr = typename PointCloudTarget::Ptr;
105  using PointCloudTargetConstPtr = typename PointCloudTarget::ConstPtr;
106 
109 
110  using Ptr = shared_ptr<IterativeClosestPoint<PointSource, PointTarget, Scalar> >;
111  using ConstPtr = shared_ptr<const IterativeClosestPoint<PointSource, PointTarget, Scalar> >;
112 
135 
138 
139  /** \brief Empty constructor. */
141  : x_idx_offset_ (0)
142  , y_idx_offset_ (0)
143  , z_idx_offset_ (0)
144  , nx_idx_offset_ (0)
145  , ny_idx_offset_ (0)
146  , nz_idx_offset_ (0)
148  , source_has_normals_ (false)
149  , target_has_normals_ (false)
150  {
151  reg_name_ = "IterativeClosestPoint";
155  };
156 
157  /** \brief Empty destructor */
159 
160  /** \brief Returns a pointer to the DefaultConvergenceCriteria used by the IterativeClosestPoint class.
161  * This allows to check the convergence state after the align() method as well as to configure
162  * DefaultConvergenceCriteria's parameters not available through the ICP API before the align()
163  * method is called. Please note that the align method sets max_iterations_,
164  * euclidean_fitness_epsilon_ and transformation_epsilon_ and therefore overrides the default / set
165  * values of the DefaultConvergenceCriteria instance.
166  * \return Pointer to the IterativeClosestPoint's DefaultConvergenceCriteria.
167  */
170  {
171  return convergence_criteria_;
172  }
173 
174  /** \brief Provide a pointer to the input source
175  * (e.g., the point cloud that we want to align to the target)
176  *
177  * \param[in] cloud the input point cloud source
178  */
179  void
180  setInputSource (const PointCloudSourceConstPtr &cloud) override
181  {
183  const auto fields = pcl::getFields<PointSource> ();
184  source_has_normals_ = false;
185  for (const auto &field : fields)
186  {
187  if (field.name == "x") x_idx_offset_ = field.offset;
188  else if (field.name == "y") y_idx_offset_ = field.offset;
189  else if (field.name == "z") z_idx_offset_ = field.offset;
190  else if (field.name == "normal_x")
191  {
192  source_has_normals_ = true;
193  nx_idx_offset_ = field.offset;
194  }
195  else if (field.name == "normal_y")
196  {
197  source_has_normals_ = true;
198  ny_idx_offset_ = field.offset;
199  }
200  else if (field.name == "normal_z")
201  {
202  source_has_normals_ = true;
203  nz_idx_offset_ = field.offset;
204  }
205  }
206  }
207 
208  /** \brief Provide a pointer to the input target
209  * (e.g., the point cloud that we want to align to the target)
210  *
211  * \param[in] cloud the input point cloud target
212  */
213  void
214  setInputTarget (const PointCloudTargetConstPtr &cloud) override
215  {
217  const auto fields = pcl::getFields<PointSource> ();
218  target_has_normals_ = false;
219  for (const auto &field : fields)
220  {
221  if (field.name == "normal_x" || field.name == "normal_y" || field.name == "normal_z")
222  {
223  target_has_normals_ = true;
224  break;
225  }
226  }
227  }
228 
229  /** \brief Set whether to use reciprocal correspondence or not
230  *
231  * \param[in] use_reciprocal_correspondence whether to use reciprocal correspondence or not
232  */
233  inline void
234  setUseReciprocalCorrespondences (bool use_reciprocal_correspondence)
235  {
236  use_reciprocal_correspondence_ = use_reciprocal_correspondence;
237  }
238 
239  /** \brief Obtain whether reciprocal correspondence are used or not */
240  inline bool
242  {
244  }
245 
246  protected:
247 
248  /** \brief Apply a rigid transform to a given dataset. Here we check whether whether
249  * the dataset has surface normals in addition to XYZ, and rotate normals as well.
250  * \param[in] input the input point cloud
251  * \param[out] output the resultant output point cloud
252  * \param[in] transform a 4x4 rigid transformation
253  * \note Can be used with cloud_in equal to cloud_out
254  */
255  virtual void
256  transformCloud (const PointCloudSource &input,
257  PointCloudSource &output,
258  const Matrix4 &transform);
259 
260  /** \brief Rigid transformation computation method with initial guess.
261  * \param output the transformed input point cloud dataset using the rigid transformation found
262  * \param guess the initial guess of the transformation to compute
263  */
264  void
265  computeTransformation (PointCloudSource &output, const Matrix4 &guess) override;
266 
267  /** \brief Looks at the Estimators and Rejectors and determines whether their blob-setter methods need to be called */
268  virtual void
270 
271  /** \brief XYZ fields offset. */
273 
274  /** \brief Normal fields offset. */
276 
277  /** \brief The correspondence type used for correspondence estimation. */
279 
280  /** \brief Internal check whether source dataset has normals or not. */
282  /** \brief Internal check whether target dataset has normals or not. */
284 
285  /** \brief Checks for whether estimators and rejectors need various data */
287  };
288 
289  /** \brief @b IterativeClosestPointWithNormals is a special case of
290  * IterativeClosestPoint, that uses a transformation estimated based on
291  * Point to Plane distances by default.
292  *
293  * By default, this implementation uses the traditional point to plane objective
294  * and computes point to plane distances using the normals of the target point
295  * cloud. It also provides the option (through setUseSymmetricObjective) of
296  * using the symmetric objective function of [Rusinkiewicz 2019]. This objective
297  * uses the normals of both the source and target point cloud and has a similar
298  * computational cost to the traditional point to plane objective while also
299  * offering improved convergence speed and a wider basin of convergence.
300  *
301  * Note that this implementation not demean the point clouds which can lead
302  * to increased numerical error. If desired, a user can demean the point cloud,
303  * run iterative closest point, and composite the resulting ICP transformation
304  * with the translations from demeaning to obtain a transformation between
305  * the original point clouds.
306  *
307  * \author Radu B. Rusu, Matthew Cong
308  * \ingroup registration
309  */
310  template <typename PointSource, typename PointTarget, typename Scalar = float>
311  class IterativeClosestPointWithNormals : public IterativeClosestPoint<PointSource, PointTarget, Scalar>
312  {
313  public:
317 
321 
322  using Ptr = shared_ptr<IterativeClosestPoint<PointSource, PointTarget, Scalar> >;
323  using ConstPtr = shared_ptr<const IterativeClosestPoint<PointSource, PointTarget, Scalar> >;
324 
325  /** \brief Empty constructor. */
327  {
328  reg_name_ = "IterativeClosestPointWithNormals";
329  setUseSymmetricObjective (false);
331  //correspondence_rejectors_.add
332  };
333 
334  /** \brief Empty destructor */
336 
337  /** \brief Set whether to use a symmetric objective function or not
338  *
339  * \param[in] use_symmetric_objective whether to use a symmetric objective function or not
340  */
341  inline void
342  setUseSymmetricObjective (bool use_symmetric_objective)
343  {
344  use_symmetric_objective_ = use_symmetric_objective;
346  {
347  auto symmetric_transformation_estimation = pcl::make_shared<pcl::registration::TransformationEstimationSymmetricPointToPlaneLLS<PointSource, PointTarget, Scalar> > ();
348  symmetric_transformation_estimation->setEnforceSameDirectionNormals (enforce_same_direction_normals_);
349  transformation_estimation_ = symmetric_transformation_estimation;
350  }
351  else
352  {
354  }
355  }
356 
357  /** \brief Obtain whether a symmetric objective is used or not */
358  inline bool
360  {
362  }
363 
364  /** \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.
365  *
366  * \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.
367  */
368  inline void
369  setEnforceSameDirectionNormals (bool enforce_same_direction_normals)
370  {
371  enforce_same_direction_normals_ = enforce_same_direction_normals;
372  auto symmetric_transformation_estimation = dynamic_pointer_cast<pcl::registration::TransformationEstimationSymmetricPointToPlaneLLS<PointSource, PointTarget, Scalar> >(transformation_estimation_);
373  if (symmetric_transformation_estimation)
374  symmetric_transformation_estimation->setEnforceSameDirectionNormals (enforce_same_direction_normals_);
375  }
376 
377  /** \brief Obtain whether source or target normals are negated on a per-point basis such that they point in the same direction or not */
378  inline bool
380  {
382  }
383 
384  protected:
385 
386  /** \brief Apply a rigid transform to a given dataset
387  * \param[in] input the input point cloud
388  * \param[out] output the resultant output point cloud
389  * \param[in] transform a 4x4 rigid transformation
390  * \note Can be used with cloud_in equal to cloud_out
391  */
392  virtual void
393  transformCloud (const PointCloudSource &input,
394  PointCloudSource &output,
395  const Matrix4 &transform);
396 
397  /** \brief Type of objective function (asymmetric vs. symmetric) used for transform estimation */
399  /** \brief Whether or not to negate source and/or target normals such that they point in the same direction in the symmetric objective function */
401  };
402 
403 }
404 
405 #include <pcl/registration/impl/icp.hpp>
pcl::IterativeClosestPoint< PointSource, PointTarget >::Ptr
shared_ptr< IterativeClosestPoint< PointSource, PointTarget, float > > Ptr
Definition: icp.h:110
pcl::IterativeClosestPointWithNormals::use_symmetric_objective_
bool use_symmetric_objective_
Type of objective function (asymmetric vs.
Definition: icp.h:398
pcl::IterativeClosestPoint::getConvergeCriteria
pcl::registration::DefaultConvergenceCriteria< Scalar >::Ptr getConvergeCriteria()
Returns a pointer to the DefaultConvergenceCriteria used by the IterativeClosestPoint class.
Definition: icp.h:169
pcl::Registration< PointSource, PointTarget, float >::correspondences_
CorrespondencesPtr correspondences_
The set of correspondences determined at this ICP step.
Definition: registration.h:557
pcl
Definition: convolution.h:46
pcl::registration::TransformationEstimationSVD
TransformationEstimationSVD implements SVD-based estimation of the transformation aligning the given ...
Definition: transformation_estimation_svd.h:59
pcl::IterativeClosestPoint::setUseReciprocalCorrespondences
void setUseReciprocalCorrespondences(bool use_reciprocal_correspondence)
Set whether to use reciprocal correspondence or not.
Definition: icp.h:234
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:62
pcl::IterativeClosestPoint::y_idx_offset_
std::size_t y_idx_offset_
Definition: icp.h:272
pcl::registration::DefaultConvergenceCriteria
DefaultConvergenceCriteria represents an instantiation of ConvergenceCriteria, and implements the fol...
Definition: default_convergence_criteria.h:67
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:191
pcl::IterativeClosestPoint::need_target_blob_
bool need_target_blob_
Definition: icp.h:286
pcl::IterativeClosestPoint< PointSource, PointTarget >::PointCloudSourcePtr
typename PointCloudSource::Ptr PointCloudSourcePtr
Definition: icp.h:100
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:294
pcl::IterativeClosestPointWithNormals::IterativeClosestPointWithNormals
IterativeClosestPointWithNormals()
Empty constructor.
Definition: icp.h:326
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:342
pcl::Registration< PointSource, PointTarget, float >::transformation_
Matrix4 transformation_
The transformation matrix estimated by the registration method.
Definition: registration.h:516
pcl::IterativeClosestPoint::use_reciprocal_correspondence_
bool use_reciprocal_correspondence_
The correspondence type used for correspondence estimation.
Definition: icp.h:278
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:400
pcl::IterativeClosestPoint< PointSource, PointTarget >::PointCloudTarget
typename Registration< PointSource, PointTarget, float >::PointCloudTarget PointCloudTarget
Definition: icp.h:103
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:180
pcl::IterativeClosestPointWithNormals::getUseSymmetricObjective
bool getUseSymmetricObjective() const
Obtain whether a symmetric objective is used or not.
Definition: icp.h:359
pcl::PointCloud< PointSource >
pcl::Registration::Matrix4
Eigen::Matrix< Scalar, 4, 4 > Matrix4
Definition: registration.h:64
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:257
pcl::IterativeClosestPoint< PointSource, PointTarget >::PointCloudSource
typename Registration< PointSource, PointTarget, float >::PointCloudSource PointCloudSource
Definition: icp.h:99
pcl::registration::DefaultConvergenceCriteria::Ptr
shared_ptr< DefaultConvergenceCriteria< Scalar > > Ptr
Definition: default_convergence_criteria.h:69
pcl::IterativeClosestPointWithNormals::~IterativeClosestPointWithNormals
virtual ~IterativeClosestPointWithNormals()
Empty destructor.
Definition: icp.h:335
pcl::IterativeClosestPoint< PointSource, PointTarget >::PointCloudTargetConstPtr
typename PointCloudTarget::ConstPtr PointCloudTargetConstPtr
Definition: icp.h:105
pcl::IterativeClosestPoint::nx_idx_offset_
std::size_t nx_idx_offset_
Normal fields offset.
Definition: icp.h:275
pcl::PointIndices::ConstPtr
shared_ptr< const ::pcl::PointIndices > ConstPtr
Definition: PointIndices.h:16
pcl::IterativeClosestPointWithNormals::Ptr
shared_ptr< IterativeClosestPoint< PointSource, PointTarget, Scalar > > Ptr
Definition: icp.h:322
pcl::IterativeClosestPointWithNormals
IterativeClosestPointWithNormals is a special case of IterativeClosestPoint, that uses a transformati...
Definition: icp.h:312
pcl::IterativeClosestPointWithNormals::ConstPtr
shared_ptr< const IterativeClosestPoint< PointSource, PointTarget, Scalar > > ConstPtr
Definition: icp.h:323
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:214
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:379
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:369
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:52
pcl::IterativeClosestPoint::convergence_criteria_
pcl::registration::DefaultConvergenceCriteria< Scalar >::Ptr convergence_criteria_
Definition: icp.h:136
pcl::IterativeClosestPoint< PointSource, PointTarget >::PointCloudSourceConstPtr
typename PointCloudSource::ConstPtr PointCloudSourceConstPtr
Definition: icp.h:101
pcl::IterativeClosestPoint::ny_idx_offset_
std::size_t ny_idx_offset_
Definition: icp.h:275
pcl::registration::CorrespondenceEstimation
CorrespondenceEstimation represents the base class for determining correspondences between target and...
Definition: correspondence_estimation.h:363
pcl::IterativeClosestPoint::IterativeClosestPoint
IterativeClosestPoint()
Empty constructor.
Definition: icp.h:140
pcl::PointIndices::Ptr
shared_ptr< ::pcl::PointIndices > Ptr
Definition: PointIndices.h:15
pcl::IterativeClosestPoint::z_idx_offset_
std::size_t z_idx_offset_
Definition: icp.h:272
pcl::Registration< PointSource, PointTarget, float >::nr_iterations_
int nr_iterations_
The number of iterations the internal optimization ran for (used internally).
Definition: registration.h:499
pcl::IterativeClosestPoint::nz_idx_offset_
std::size_t nz_idx_offset_
Definition: icp.h:275
pcl::IterativeClosestPoint::computeTransformation
void computeTransformation(PointCloudSource &output, const Matrix4 &guess) override
Rigid transformation computation method with initial guess.
Definition: icp.hpp:121
pcl::IterativeClosestPoint::need_source_blob_
bool need_source_blob_
Checks for whether estimators and rejectors need various data.
Definition: icp.h:286
pcl::IterativeClosestPointWithNormals::PointCloudSource
typename IterativeClosestPoint< PointSource, PointTarget, Scalar >::PointCloudSource PointCloudSource
Definition: icp.h:314
pcl::IterativeClosestPoint::~IterativeClosestPoint
~IterativeClosestPoint()
Empty destructor.
Definition: icp.h:158
pcl::IterativeClosestPointWithNormals::Matrix4
typename IterativeClosestPoint< PointSource, PointTarget, Scalar >::Matrix4 Matrix4
Definition: icp.h:316
pcl::PCLBase< PointSource >::PointIndicesPtr
PointIndices::Ptr PointIndicesPtr
Definition: pcl_base.h:76
pcl::IterativeClosestPoint< PointSource, PointTarget >::PointCloudTargetPtr
typename PointCloudTarget::Ptr PointCloudTargetPtr
Definition: icp.h:104
pcl::Registration< PointSource, PointTarget, float >::transformation_estimation_
TransformationEstimationPtr transformation_estimation_
A TransformationEstimation object, used to calculate the 4x4 rigid transformation.
Definition: registration.h:560
pcl::IterativeClosestPoint::source_has_normals_
bool source_has_normals_
Internal check whether source dataset has normals or not.
Definition: icp.h:281
pcl::IterativeClosestPoint::target_has_normals_
bool target_has_normals_
Internal check whether target dataset has normals or not.
Definition: icp.h:283
pcl::IterativeClosestPoint< PointSource, PointTarget >::Matrix4
typename Registration< PointSource, PointTarget, float >::Matrix4 Matrix4
Definition: icp.h:137
pcl::IterativeClosestPoint< PointSource, PointTarget >::ConstPtr
shared_ptr< const IterativeClosestPoint< PointSource, PointTarget, float > > ConstPtr
Definition: icp.h:111
pcl::IterativeClosestPoint::getUseReciprocalCorrespondences
bool getUseReciprocalCorrespondences() const
Obtain whether reciprocal correspondence are used or not.
Definition: icp.h:241
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:490
pcl::registration::TransformationEstimationPointToPlaneLLS
TransformationEstimationPointToPlaneLLS implements a Linear Least Squares (LLS) approximation for min...
Definition: transformation_estimation_point_to_plane_lls.h:64
pcl::IterativeClosestPointWithNormals::PointCloudTarget
typename IterativeClosestPoint< PointSource, PointTarget, Scalar >::PointCloudTarget PointCloudTarget
Definition: icp.h:315
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:563
pcl::IterativeClosestPoint::x_idx_offset_
std::size_t x_idx_offset_
XYZ fields offset.
Definition: icp.h:272