Point Cloud Library (PCL) 1.14.0
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transformation_estimation_point_to_plane_weighted.h
1/*
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3 *
4 * Point Cloud Library (PCL) - www.pointclouds.org
5 * Copyright (c) 2009-2012, Willow Garage, Inc.
6 * Copyright (c) 2012-, Open Perception, Inc.
7 * Copyright (c) Alexandru-Eugen Ichim
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38
39#pragma once
40
41#include <pcl/registration/distances.h>
42#include <pcl/registration/transformation_estimation_point_to_plane.h>
43#include <pcl/registration/warp_point_rigid.h>
44#include <pcl/memory.h>
45#include <pcl/pcl_macros.h>
46
47namespace pcl {
48namespace registration {
49/** @b TransformationEstimationPointToPlaneWeighted uses Levenberg Marquardt
50 * optimization to find the transformation that minimizes the point-to-plane distance
51 * between the given correspondences. In addition to the
52 * TransformationEstimationPointToPlane class, this version takes per-correspondence
53 * weights and optimizes accordingly.
54 *
55 * \author Alexandru-Eugen Ichim
56 * \ingroup registration
57 */
58template <typename PointSource, typename PointTarget, typename MatScalar = float>
60: public TransformationEstimationPointToPlane<PointSource, PointTarget, MatScalar> {
62 using PointCloudSourcePtr = typename PointCloudSource::Ptr;
63 using PointCloudSourceConstPtr = typename PointCloudSource::ConstPtr;
64
66
67 using PointIndicesPtr = PointIndices::Ptr;
68 using PointIndicesConstPtr = PointIndices::ConstPtr;
69
70public:
71 using Ptr = shared_ptr<TransformationEstimationPointToPlaneWeighted<PointSource,
72 PointTarget,
73 MatScalar>>;
74 using ConstPtr =
75 shared_ptr<const TransformationEstimationPointToPlaneWeighted<PointSource,
76 PointTarget,
77 MatScalar>>;
78
79 using VectorX = Eigen::Matrix<MatScalar, Eigen::Dynamic, 1>;
80 using Vector4 = Eigen::Matrix<MatScalar, 4, 1>;
81 using Matrix4 =
83
84 /** \brief Constructor. */
86
87 /** \brief Copy constructor.
88 * \param[in] src the TransformationEstimationPointToPlaneWeighted object to copy into
89 * this
90 */
100
101 /** \brief Copy operator.
102 * \param[in] src the TransformationEstimationPointToPlaneWeighted object to copy into
103 * this
104 */
117
118 /** \brief Destructor. */
120
121 /** \brief Estimate a rigid rotation transformation between a source and a target
122 * point cloud using LM. \param[in] cloud_src the source point cloud dataset
123 * \param[in] cloud_tgt the target point cloud dataset
124 * \param[out] transformation_matrix the resultant transformation matrix
125 * \note Uses the weights given by setWeights.
126 */
127 inline void
129 const pcl::PointCloud<PointTarget>& cloud_tgt,
130 Matrix4& transformation_matrix) const;
131
132 /** \brief Estimate a rigid rotation transformation between a source and a target
133 * point cloud using LM. \param[in] cloud_src the source point cloud dataset
134 * \param[in] indices_src the vector of indices describing the points of interest in
135 * \a cloud_src
136 * \param[in] cloud_tgt the target point cloud dataset
137 * \param[out] transformation_matrix the resultant transformation matrix
138 * \note Uses the weights given by setWeights.
139 */
140 inline void
142 const pcl::Indices& indices_src,
143 const pcl::PointCloud<PointTarget>& cloud_tgt,
144 Matrix4& transformation_matrix) const;
145
146 /** \brief Estimate a rigid rotation transformation between a source and a target
147 * point cloud using LM. \param[in] cloud_src the source point cloud dataset
148 * \param[in] indices_src the vector of indices describing the points of interest in
149 * \a cloud_src
150 * \param[in] cloud_tgt the target point cloud dataset
151 * \param[in] indices_tgt the vector of indices describing the correspondences of the
152 * interest points from \a indices_src
153 * \param[out] transformation_matrix the resultant transformation matrix
154 * \note Uses the weights given by setWeights.
155 */
156 void
158 const pcl::Indices& indices_src,
159 const pcl::PointCloud<PointTarget>& cloud_tgt,
160 const pcl::Indices& indices_tgt,
161 Matrix4& transformation_matrix) const;
162
163 /** \brief Estimate a rigid rotation transformation between a source and a target
164 * point cloud using LM. \param[in] cloud_src the source point cloud dataset
165 * \param[in] cloud_tgt the target point cloud dataset
166 * \param[in] correspondences the vector of correspondences between source and target
167 * point cloud \param[out] transformation_matrix the resultant transformation matrix
168 * \note Uses the weights given by setWeights.
169 */
170 void
172 const pcl::PointCloud<PointTarget>& cloud_tgt,
173 const pcl::Correspondences& correspondences,
174 Matrix4& transformation_matrix) const;
175
176 inline void
177 setWeights(const std::vector<double>& weights)
178 {
179 correspondence_weights_ = weights;
180 }
181
182 /// use the weights given in the pcl::CorrespondencesPtr for one of the
183 /// estimateTransformation (...) methods
184 inline void
185 setUseCorrespondenceWeights(bool use_correspondence_weights)
186 {
187 use_correspondence_weights_ = use_correspondence_weights;
188 }
189
190 /** \brief Set the function we use to warp points. Defaults to rigid 6D warp.
191 * \param[in] warp_fcn a shared pointer to an object that warps points
192 */
193 void
196 {
197 warp_point_ = warp_fcn;
198 }
199
200protected:
202 mutable std::vector<double> correspondence_weights_{};
203
204 /** \brief Temporary pointer to the source dataset. */
205 mutable const PointCloudSource* tmp_src_{nullptr};
206
207 /** \brief Temporary pointer to the target dataset. */
208 mutable const PointCloudTarget* tmp_tgt_{nullptr};
209
210 /** \brief Temporary pointer to the source dataset indices. */
211 mutable const pcl::Indices* tmp_idx_src_{nullptr};
212
213 /** \brief Temporary pointer to the target dataset indices. */
214 mutable const pcl::Indices* tmp_idx_tgt_{nullptr};
215
216 /** \brief The parameterized function used to warp the source to the target. */
219
220 /** Base functor all the models that need non linear optimization must
221 * define their own one and implement operator() (const Eigen::VectorXd& x,
222 * Eigen::VectorXd& fvec) or operator() (const Eigen::VectorXf& x, Eigen::VectorXf&
223 * fvec) depending on the chosen _Scalar
224 */
225 template <typename _Scalar, int NX = Eigen::Dynamic, int NY = Eigen::Dynamic>
226 struct Functor {
227 using Scalar = _Scalar;
229 using InputType = Eigen::Matrix<_Scalar, InputsAtCompileTime, 1>;
230 using ValueType = Eigen::Matrix<_Scalar, ValuesAtCompileTime, 1>;
232 Eigen::Matrix<_Scalar, ValuesAtCompileTime, InputsAtCompileTime>;
233
234 /** \brief Empty Constructor. */
236
237 /** \brief Constructor
238 * \param[in] m_data_points number of data points to evaluate.
239 */
240 Functor(int m_data_points) : m_data_points_(m_data_points) {}
241
242 /** \brief Destructor. */
243 virtual ~Functor() = default;
244
245 /** \brief Get the number of values. */
246 int
247 values() const
248 {
249 return (m_data_points_);
250 }
251
252 protected:
254 };
255
256 struct OptimizationFunctor : public Functor<MatScalar> {
257 using Functor<MatScalar>::values;
258
259 /** Functor constructor
260 * \param[in] m_data_points the number of data points to evaluate
261 * \param[in,out] estimator pointer to the estimator object
262 */
263 OptimizationFunctor(int m_data_points,
265 : Functor<MatScalar>(m_data_points), estimator_(estimator)
266 {}
267
268 /** Copy constructor
269 * \param[in] src the optimization functor to copy into this
270 */
272 : Functor<MatScalar>(src.m_data_points_), estimator_()
273 {
274 *this = src;
275 }
276
277 /** Copy operator
278 * \param[in] src the optimization functor to copy into this
279 */
280 inline OptimizationFunctor&
282 {
285 return (*this);
286 }
287
288 /** \brief Destructor. */
289 virtual ~OptimizationFunctor() = default;
290
291 /** Fill fvec from x. For the current state vector x fill the f values
292 * \param[in] x state vector
293 * \param[out] fvec f values vector
294 */
295 int
296 operator()(const VectorX& x, VectorX& fvec) const;
297
299 PointTarget,
300 MatScalar>* estimator_;
301 };
302
303 struct OptimizationFunctorWithIndices : public Functor<MatScalar> {
304 using Functor<MatScalar>::values;
305
306 /** Functor constructor
307 * \param[in] m_data_points the number of data points to evaluate
308 * \param[in,out] estimator pointer to the estimator object
309 */
311 int m_data_points,
313 : Functor<MatScalar>(m_data_points), estimator_(estimator)
314 {}
315
316 /** Copy constructor
317 * \param[in] src the optimization functor to copy into this
318 */
320 : Functor<MatScalar>(src.m_data_points_), estimator_()
321 {
322 *this = src;
323 }
324
325 /** Copy operator
326 * \param[in] src the optimization functor to copy into this
327 */
330 {
333 return (*this);
334 }
335
336 /** \brief Destructor. */
338
339 /** Fill fvec from x. For the current state vector x fill the f values
340 * \param[in] x state vector
341 * \param[out] fvec f values vector
342 */
343 int
344 operator()(const VectorX& x, VectorX& fvec) const;
345
347 PointTarget,
348 MatScalar>* estimator_;
349 };
350
351public:
353};
354} // namespace registration
355} // namespace pcl
356
357#include <pcl/registration/impl/transformation_estimation_point_to_plane_weighted.hpp>
shared_ptr< PointCloud< PointSource > > Ptr
shared_ptr< const PointCloud< PointSource > > ConstPtr
TransformationEstimationPointToPlane uses Levenberg Marquardt optimization to find the transformation...
TransformationEstimationPointToPlaneWeighted uses Levenberg Marquardt optimization to find the transf...
void setWarpFunction(const typename WarpPointRigid< PointSource, PointTarget, MatScalar >::Ptr &warp_fcn)
Set the function we use to warp points.
TransformationEstimationPointToPlaneWeighted(const TransformationEstimationPointToPlaneWeighted &src)
Copy constructor.
void setUseCorrespondenceWeights(bool use_correspondence_weights)
use the weights given in the pcl::CorrespondencesPtr for one of the estimateTransformation (....
void estimateRigidTransformation(const pcl::PointCloud< PointSource > &cloud_src, const pcl::PointCloud< PointTarget > &cloud_tgt, Matrix4 &transformation_matrix) const
Estimate a rigid rotation transformation between a source and a target point cloud using LM.
typename TransformationEstimation< PointSource, PointTarget, MatScalar >::Matrix4 Matrix4
TransformationEstimationPointToPlaneWeighted & operator=(const TransformationEstimationPointToPlaneWeighted &src)
Copy operator.
const pcl::Indices * tmp_idx_tgt_
Temporary pointer to the target dataset indices.
const pcl::Indices * tmp_idx_src_
Temporary pointer to the source dataset indices.
shared_ptr< const TransformationEstimationPointToPlaneWeighted< PointSource, PointTarget, MatScalar > > ConstPtr
virtual ~TransformationEstimationPointToPlaneWeighted()=default
Destructor.
pcl::registration::WarpPointRigid< PointSource, PointTarget, MatScalar >::Ptr warp_point_
The parameterized function used to warp the source to the target.
shared_ptr< TransformationEstimationPointToPlaneWeighted< PointSource, PointTarget, MatScalar > > Ptr
shared_ptr< WarpPointRigid< PointSourceT, PointTargetT, Scalar > > Ptr
#define PCL_MAKE_ALIGNED_OPERATOR_NEW
Macro to signal a class requires a custom allocator.
Definition memory.h:63
Defines functions, macros and traits for allocating and using memory.
std::vector< pcl::Correspondence, Eigen::aligned_allocator< pcl::Correspondence > > Correspondences
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition types.h:133
Defines all the PCL and non-PCL macros used.
shared_ptr< ::pcl::PointIndices > Ptr
shared_ptr< const ::pcl::PointIndices > ConstPtr
Base functor all the models that need non linear optimization must define their own one and implement...
OptimizationFunctor(int m_data_points, const TransformationEstimationPointToPlaneWeighted *estimator)
Functor constructor.
const TransformationEstimationPointToPlaneWeighted< PointSource, PointTarget, MatScalar > * estimator_
const TransformationEstimationPointToPlaneWeighted< PointSource, PointTarget, MatScalar > * estimator_
OptimizationFunctorWithIndices & operator=(const OptimizationFunctorWithIndices &src)
Copy operator.
OptimizationFunctorWithIndices(int m_data_points, const TransformationEstimationPointToPlaneWeighted *estimator)
Functor constructor.