Point Cloud Library (PCL) 1.14.0
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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#include <pcl/pcl_config.h> // for PCL_NO_PRECOMPILE
52
53namespace pcl {
54/** \brief @b IterativeClosestPoint provides a base implementation of the Iterative
55 * Closest Point algorithm. The transformation is estimated based on Singular Value
56 * 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
62 * (via \ref setMaximumIterations)</li> <li>The epsilon (difference) between the
63 * previous transformation and the current estimated transformation is smaller than an
64 * user imposed value (via \ref setTransformationEpsilon)</li> <li>The sum of Euclidean
65 * squared errors is smaller than a user defined threshold (via \ref
66 * setEuclideanFitnessEpsilon)</li>
67 * </ol>
68 *
69 *
70 * Usage example:
71 * \code
72 * IterativeClosestPoint<PointXYZ, PointXYZ> icp;
73 * // Set the input source and target
74 * icp.setInputSource (cloud_source);
75 * icp.setInputTarget (cloud_target);
76 *
77 * // Set the max correspondence distance to 5cm (e.g., correspondences with higher
78 * // distances will be ignored)
79 * icp.setMaxCorrespondenceDistance (0.05);
80 * // Set the maximum number of iterations (criterion 1)
81 * icp.setMaximumIterations (50);
82 * // Set the transformation epsilon (criterion 2)
83 * icp.setTransformationEpsilon (1e-8);
84 * // Set the euclidean distance difference epsilon (criterion 3)
85 * icp.setEuclideanFitnessEpsilon (1);
86 *
87 * // Perform the alignment
88 * icp.align (cloud_source_registered);
89 *
90 * // Obtain the transformation that aligned cloud_source to cloud_source_registered
91 * Eigen::Matrix4f transformation = icp.getFinalTransformation ();
92 * \endcode
93 *
94 * \author Radu B. Rusu, Michael Dixon
95 * \ingroup registration
96 */
97template <typename PointSource, typename PointTarget, typename Scalar = float>
98class IterativeClosestPoint : public Registration<PointSource, PointTarget, Scalar> {
99public:
102 using PointCloudSourcePtr = typename PointCloudSource::Ptr;
103 using PointCloudSourceConstPtr = typename PointCloudSource::ConstPtr;
104
107 using PointCloudTargetPtr = typename PointCloudTarget::Ptr;
108 using PointCloudTargetConstPtr = typename PointCloudTarget::ConstPtr;
109
112
113 using Ptr = shared_ptr<IterativeClosestPoint<PointSource, PointTarget, Scalar>>;
114 using ConstPtr =
115 shared_ptr<const IterativeClosestPoint<PointSource, PointTarget, Scalar>>;
116
117 using Registration<PointSource, PointTarget, Scalar>::reg_name_;
118 using Registration<PointSource, PointTarget, Scalar>::getClassName;
119 using Registration<PointSource, PointTarget, Scalar>::input_;
120 using Registration<PointSource, PointTarget, Scalar>::indices_;
121 using Registration<PointSource, PointTarget, Scalar>::target_;
122 using Registration<PointSource, PointTarget, Scalar>::nr_iterations_;
123 using Registration<PointSource, PointTarget, Scalar>::max_iterations_;
124 using Registration<PointSource, PointTarget, Scalar>::previous_transformation_;
125 using Registration<PointSource, PointTarget, Scalar>::final_transformation_;
126 using Registration<PointSource, PointTarget, Scalar>::transformation_;
127 using Registration<PointSource, PointTarget, Scalar>::transformation_epsilon_;
128 using Registration<PointSource, PointTarget, Scalar>::
130 using Registration<PointSource, PointTarget, Scalar>::converged_;
131 using Registration<PointSource, PointTarget, Scalar>::corr_dist_threshold_;
132 using Registration<PointSource, PointTarget, Scalar>::inlier_threshold_;
133 using Registration<PointSource, PointTarget, Scalar>::min_number_correspondences_;
134 using Registration<PointSource, PointTarget, Scalar>::update_visualizer_;
135 using Registration<PointSource, PointTarget, Scalar>::euclidean_fitness_epsilon_;
136 using Registration<PointSource, PointTarget, Scalar>::correspondences_;
137 using Registration<PointSource, PointTarget, Scalar>::transformation_estimation_;
138 using Registration<PointSource, PointTarget, Scalar>::correspondence_estimation_;
139 using Registration<PointSource, PointTarget, Scalar>::correspondence_rejectors_;
140
144
145 /** \brief Empty constructor. */
147 {
148 reg_name_ = "IterativeClosestPoint";
151 TransformationEstimationSVD<PointSource, PointTarget, Scalar>());
158 };
159
160 /**
161 * \brief Due to `convergence_criteria_` holding references to the class members,
162 * it is tricky to correctly implement its copy and move operations correctly. This
163 * can result in subtle bugs and to prevent them, these operations for ICP have
164 * been disabled.
165 *
166 * \todo: remove deleted ctors and assignments operations after resolving the issue
167 */
174
175 /** \brief Empty destructor */
176 ~IterativeClosestPoint() override = default;
177
178 /** \brief Returns a pointer to the DefaultConvergenceCriteria used by the
179 * IterativeClosestPoint class. This allows to check the convergence state after the
180 * align() method as well as to configure DefaultConvergenceCriteria's parameters not
181 * available through the ICP API before the align() method is called. Please note that
182 * the align method sets max_iterations_, euclidean_fitness_epsilon_ and
183 * transformation_epsilon_ and therefore overrides the default / set values of the
184 * DefaultConvergenceCriteria instance. \return Pointer to the IterativeClosestPoint's
185 * DefaultConvergenceCriteria.
186 */
192
193 /** \brief Provide a pointer to the input source
194 * (e.g., the point cloud that we want to align to the target)
195 *
196 * \param[in] cloud the input point cloud source
197 */
198 void
200 {
202 const auto fields = pcl::getFields<PointSource>();
203 source_has_normals_ = false;
204 for (const auto& field : fields) {
205 if (field.name == "x")
206 x_idx_offset_ = field.offset;
207 else if (field.name == "y")
208 y_idx_offset_ = field.offset;
209 else if (field.name == "z")
210 z_idx_offset_ = field.offset;
211 else if (field.name == "normal_x") {
212 source_has_normals_ = true;
213 nx_idx_offset_ = field.offset;
214 }
215 else if (field.name == "normal_y") {
216 source_has_normals_ = true;
217 ny_idx_offset_ = field.offset;
218 }
219 else if (field.name == "normal_z") {
220 source_has_normals_ = true;
221 nz_idx_offset_ = field.offset;
222 }
223 }
224 }
225
226 /** \brief Provide a pointer to the input target
227 * (e.g., the point cloud that we want to align the input source to)
228 *
229 * \param[in] cloud the input point cloud target
230 */
231 void
233 {
235 const auto fields = pcl::getFields<PointSource>();
236 target_has_normals_ = false;
237 for (const auto& field : fields) {
238 if (field.name == "normal_x" || field.name == "normal_y" ||
239 field.name == "normal_z") {
240 target_has_normals_ = true;
241 break;
242 }
243 }
244 }
245
246 /** \brief Set whether to use reciprocal correspondence or not
247 *
248 * \param[in] use_reciprocal_correspondence whether to use reciprocal correspondence
249 * or not
250 */
251 inline void
252 setUseReciprocalCorrespondences(bool use_reciprocal_correspondence)
253 {
254 use_reciprocal_correspondence_ = use_reciprocal_correspondence;
255 }
256
257 /** \brief Obtain whether reciprocal correspondence are used or not */
258 inline bool
263
264protected:
265 /** \brief Apply a rigid transform to a given dataset. Here we check whether
266 * the dataset has surface normals in addition to XYZ, and rotate normals as well.
267 * \param[in] input the input point cloud
268 * \param[out] output the resultant output point cloud
269 * \param[in] transform a 4x4 rigid transformation
270 * \note Can be used with cloud_in equal to cloud_out
271 */
272 virtual void
273 transformCloud(const PointCloudSource& input,
274 PointCloudSource& output,
275 const Matrix4& transform);
276
277 /** \brief Rigid transformation computation method with initial guess.
278 * \param output the transformed input point cloud dataset using the rigid
279 * transformation found \param guess the initial guess of the transformation to
280 * compute
281 */
282 void
283 computeTransformation(PointCloudSource& output, const Matrix4& guess) override;
284
285 /** \brief Looks at the Estimators and Rejectors and determines whether their
286 * blob-setter methods need to be called */
287 virtual void
289
290 /** \brief XYZ fields offset. */
292
293 /** \brief Normal fields offset. */
295
296 /** \brief The correspondence type used for correspondence estimation. */
298
299 /** \brief Internal check whether source dataset has normals or not. */
301 /** \brief Internal check whether target dataset has normals or not. */
303
304 /** \brief Checks for whether estimators and rejectors need various data */
306};
307
308/** \brief @b IterativeClosestPointWithNormals is a special case of
309 * IterativeClosestPoint, that uses a transformation estimated based on
310 * Point to Plane distances by default.
311 *
312 * By default, this implementation uses the traditional point to plane objective
313 * and computes point to plane distances using the normals of the target point
314 * cloud. It also provides the option (through setUseSymmetricObjective) of
315 * using the symmetric objective function of [Rusinkiewicz 2019]. This objective
316 * uses the normals of both the source and target point cloud and has a similar
317 * computational cost to the traditional point to plane objective while also
318 * offering improved convergence speed and a wider basin of convergence.
319 *
320 * Note that this implementation not demean the point clouds which can lead
321 * to increased numerical error. If desired, a user can demean the point cloud,
322 * run iterative closest point, and composite the resulting ICP transformation
323 * with the translations from demeaning to obtain a transformation between
324 * the original point clouds.
325 *
326 * \author Radu B. Rusu, Matthew Cong
327 * \ingroup registration
328 */
329template <typename PointSource, typename PointTarget, typename Scalar = float>
331: public IterativeClosestPoint<PointSource, PointTarget, Scalar> {
332public:
333 using PointCloudSource = typename IterativeClosestPoint<PointSource,
334 PointTarget,
335 Scalar>::PointCloudSource;
336 using PointCloudTarget = typename IterativeClosestPoint<PointSource,
337 PointTarget,
338 Scalar>::PointCloudTarget;
339 using Matrix4 =
341
342 using IterativeClosestPoint<PointSource, PointTarget, Scalar>::reg_name_;
343 using IterativeClosestPoint<PointSource, PointTarget, Scalar>::
345 using IterativeClosestPoint<PointSource, PointTarget, Scalar>::
347
348 using Ptr =
349 shared_ptr<IterativeClosestPointWithNormals<PointSource, PointTarget, Scalar>>;
350 using ConstPtr = shared_ptr<
352
353 /** \brief Empty constructor. */
355 {
356 reg_name_ = "IterativeClosestPointWithNormals";
359 // correspondence_rejectors_.add
360 };
361
362 /** \brief Empty destructor */
364
365 /** \brief Set whether to use a symmetric objective function or not
366 *
367 * \param[in] use_symmetric_objective whether to use a symmetric objective function or
368 * not
369 */
370 inline void
371 setUseSymmetricObjective(bool use_symmetric_objective)
372 {
373 use_symmetric_objective_ = use_symmetric_objective;
375 auto symmetric_transformation_estimation = pcl::make_shared<
377 PointSource,
378 PointTarget,
379 Scalar>>();
380 symmetric_transformation_estimation->setEnforceSameDirectionNormals(
382 transformation_estimation_ = symmetric_transformation_estimation;
383 }
384 else {
387 PointTarget,
388 Scalar>());
389 }
390 }
391
392 /** \brief Obtain whether a symmetric objective is used or not */
393 inline bool
395 {
397 }
398
399 /** \brief Set whether or not to negate source or target normals on a per-point basis
400 * such that they point in the same direction. Only applicable to the symmetric
401 * objective function.
402 *
403 * \param[in] enforce_same_direction_normals whether to negate source or target
404 * normals on a per-point basis such that they point in the same direction.
405 */
406 inline void
407 setEnforceSameDirectionNormals(bool enforce_same_direction_normals)
408 {
409 enforce_same_direction_normals_ = enforce_same_direction_normals;
410 auto symmetric_transformation_estimation = dynamic_pointer_cast<
412 PointTarget,
413 Scalar>>(
415 if (symmetric_transformation_estimation)
416 symmetric_transformation_estimation->setEnforceSameDirectionNormals(
418 }
419
420 /** \brief Obtain whether source or target normals are negated on a per-point basis
421 * such that they point in the same direction or not */
422 inline bool
427
428protected:
429 /** \brief Apply a rigid transform to a given dataset
430 * \param[in] input the input point cloud
431 * \param[out] output the resultant output point cloud
432 * \param[in] transform a 4x4 rigid transformation
433 * \note Can be used with cloud_in equal to cloud_out
434 */
435 void
436 transformCloud(const PointCloudSource& input,
437 PointCloudSource& output,
438 const Matrix4& transform) override;
439
440 /** \brief Type of objective function (asymmetric vs. symmetric) used for transform
441 * estimation */
443 /** \brief Whether or not to negate source and/or target normals such that they point
444 * in the same direction in the symmetric objective function */
446};
447
448} // namespace pcl
449
450#include <pcl/registration/impl/icp.hpp>
451
452#if !defined(PCL_NO_PRECOMPILE) && !defined(PCL_REGISTRATION_ICP_CPP_)
456#endif // PCL_NO_PRECOMPILE
IterativeClosestPoint provides a base implementation of the Iterative Closest Point algorithm.
Definition icp.h:98
typename PointCloudSource::ConstPtr PointCloudSourceConstPtr
Definition icp.h:103
typename Registration< PointSource, PointTarget, Scalar >::Matrix4 Matrix4
Definition icp.h:143
std::size_t y_idx_offset_
Definition icp.h:291
std::size_t nz_idx_offset_
Definition icp.h:294
bool use_reciprocal_correspondence_
The correspondence type used for correspondence estimation.
Definition icp.h:297
void setUseReciprocalCorrespondences(bool use_reciprocal_correspondence)
Set whether to use reciprocal correspondence or not.
Definition icp.h:252
typename PointCloudTarget::ConstPtr PointCloudTargetConstPtr
Definition icp.h:108
typename Registration< PointSource, PointTarget, Scalar >::PointCloudTarget PointCloudTarget
Definition icp.h:106
std::size_t ny_idx_offset_
Definition icp.h:294
shared_ptr< IterativeClosestPoint< PointSource, PointTarget, Scalar > > Ptr
Definition icp.h:113
bool need_source_blob_
Checks for whether estimators and rejectors need various data.
Definition icp.h:305
typename PointCloudTarget::Ptr PointCloudTargetPtr
Definition icp.h:107
typename Registration< PointSource, PointTarget, Scalar >::PointCloudSource PointCloudSource
Definition icp.h:101
pcl::registration::DefaultConvergenceCriteria< Scalar >::Ptr convergence_criteria_
Definition icp.h:142
bool getUseReciprocalCorrespondences() const
Obtain whether reciprocal correspondence are used or not.
Definition icp.h:259
IterativeClosestPoint()
Empty constructor.
Definition icp.h:146
void setInputTarget(const PointCloudTargetConstPtr &cloud) override
Provide a pointer to the input target (e.g., the point cloud that we want to align the input source t...
Definition icp.h:232
PointIndices::ConstPtr PointIndicesConstPtr
Definition icp.h:111
bool source_has_normals_
Internal check whether source dataset has normals or not.
Definition icp.h:300
virtual void transformCloud(const PointCloudSource &input, PointCloudSource &output, const Matrix4 &transform)
Apply a rigid transform to a given dataset.
Definition icp.hpp:51
virtual void determineRequiredBlobData()
Looks at the Estimators and Rejectors and determines whether their blob-setter methods need to be cal...
Definition icp.hpp:272
std::size_t nx_idx_offset_
Normal fields offset.
Definition icp.h:294
IterativeClosestPoint & operator=(const IterativeClosestPoint &)=delete
IterativeClosestPoint(IterativeClosestPoint &&)=delete
void computeTransformation(PointCloudSource &output, const Matrix4 &guess) override
Rigid transformation computation method with initial guess.
Definition icp.hpp:115
IterativeClosestPoint & operator=(IterativeClosestPoint &&)=delete
PointIndices::Ptr PointIndicesPtr
Definition icp.h:110
IterativeClosestPoint(const IterativeClosestPoint &)=delete
Due to convergence_criteria_ holding references to the class members, it is tricky to correctly imple...
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:199
pcl::registration::DefaultConvergenceCriteria< Scalar >::Ptr getConvergeCriteria()
Returns a pointer to the DefaultConvergenceCriteria used by the IterativeClosestPoint class.
Definition icp.h:188
~IterativeClosestPoint() override=default
Empty destructor.
bool target_has_normals_
Internal check whether target dataset has normals or not.
Definition icp.h:302
typename PointCloudSource::Ptr PointCloudSourcePtr
Definition icp.h:102
shared_ptr< const IterativeClosestPoint< PointSource, PointTarget, Scalar > > ConstPtr
Definition icp.h:115
std::size_t z_idx_offset_
Definition icp.h:291
std::size_t x_idx_offset_
XYZ fields offset.
Definition icp.h:291
IterativeClosestPointWithNormals is a special case of IterativeClosestPoint, that uses a transformati...
Definition icp.h:331
IterativeClosestPointWithNormals()
Empty constructor.
Definition icp.h:354
void setUseSymmetricObjective(bool use_symmetric_objective)
Set whether to use a symmetric objective function or not.
Definition icp.h:371
shared_ptr< IterativeClosestPointWithNormals< PointSource, PointTarget, Scalar > > Ptr
Definition icp.h:349
typename IterativeClosestPoint< PointSource, PointTarget, Scalar >::PointCloudSource PointCloudSource
Definition icp.h:335
bool getUseSymmetricObjective() const
Obtain whether a symmetric objective is used or not.
Definition icp.h:394
bool use_symmetric_objective_
Type of objective function (asymmetric vs.
Definition icp.h:442
~IterativeClosestPointWithNormals() override=default
Empty destructor.
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:407
void transformCloud(const PointCloudSource &input, PointCloudSource &output, const Matrix4 &transform) override
Apply a rigid transform to a given dataset.
Definition icp.hpp:314
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:423
typename IterativeClosestPoint< PointSource, PointTarget, Scalar >::Matrix4 Matrix4
Definition icp.h:340
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:445
shared_ptr< const IterativeClosestPointWithNormals< PointSource, PointTarget, Scalar > > ConstPtr
Definition icp.h:351
typename IterativeClosestPoint< PointSource, PointTarget, Scalar >::PointCloudTarget PointCloudTarget
Definition icp.h:338
PointCloudConstPtr input_
The input point cloud dataset.
Definition pcl_base.h:147
IndicesPtr indices_
A pointer to the vector of point indices to use.
Definition pcl_base.h:150
Registration represents the base registration class for general purpose, ICP-like methods.
Matrix4 final_transformation_
The final transformation matrix estimated by the registration method after N iterations.
std::function< UpdateVisualizerCallbackSignature > update_visualizer_
Callback function to update intermediate source point cloud position during it's registration to the ...
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)
double corr_dist_threshold_
The maximum distance threshold between two correspondent points in source <-> target.
std::string reg_name_
The registration method name.
Matrix4 transformation_
The transformation matrix estimated by the registration method.
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...
CorrespondenceEstimationPtr correspondence_estimation_
A CorrespondenceEstimation object, used to estimate correspondences between the source and the target...
int nr_iterations_
The number of iterations the internal optimization ran for (used internally).
Matrix4 previous_transformation_
The previous transformation matrix estimated by the registration method (used internally).
bool converged_
Holds internal convergence state, given user parameters.
Eigen::Matrix< Scalar, 4, 4 > Matrix4
CorrespondencesPtr correspondences_
The set of correspondences determined at this ICP step.
TransformationEstimationPtr transformation_estimation_
A TransformationEstimation object, used to calculate the 4x4 rigid transformation.
int max_iterations_
The maximum number of iterations the internal optimization should run for.
double transformation_rotation_epsilon_
The maximum rotation difference between two consecutive transformations in order to consider converge...
unsigned int min_number_correspondences_
The minimum number of correspondences that the algorithm needs before attempting to estimate the tran...
double euclidean_fitness_epsilon_
The maximum allowed Euclidean error between two consecutive steps in the ICP loop,...
double inlier_threshold_
The inlier distance threshold for the internal RANSAC outlier rejection loop.
double transformation_epsilon_
The maximum difference between two consecutive transformations in order to consider convergence (user...
std::vector< CorrespondenceRejectorPtr > correspondence_rejectors_
The list of correspondence rejectors to use.
PointCloudTargetConstPtr target_
The input point cloud dataset target.
const std::string & getClassName() const
Abstract class get name method.
Abstract CorrespondenceEstimationBase class.
DefaultConvergenceCriteria represents an instantiation of ConvergenceCriteria, and implements the fol...
shared_ptr< DefaultConvergenceCriteria< Scalar > > Ptr
TransformationEstimationPointToPlaneLLS implements a Linear Least Squares (LLS) approximation for min...
TransformationEstimationSymmetricPointToPlaneLLS implements a Linear Least Squares (LLS) approximatio...
Defines functions, macros and traits for allocating and using memory.
shared_ptr< T > make_shared(Args &&... args)
Returns a pcl::shared_ptr compliant with type T's allocation policy.
shared_ptr< ::pcl::PointIndices > Ptr
shared_ptr< const ::pcl::PointIndices > ConstPtr