Point Cloud Library (PCL) 1.12.0
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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 */
107 {
108 tmp_src_ = src.tmp_src_;
109 tmp_tgt_ = src.tmp_tgt_;
110 tmp_idx_src_ = src.tmp_idx_src_;
111 tmp_idx_tgt_ = src.tmp_idx_tgt_;
112 warp_point_ = src.warp_point_;
113 correspondence_weights_ = src.correspondence_weights_;
114 use_correspondence_weights_ = src.use_correspondence_weights_;
115 }
116
117 /** \brief Destructor. */
119
120 /** \brief Estimate a rigid rotation transformation between a source and a target
121 * point cloud using LM. \param[in] cloud_src the source point cloud dataset
122 * \param[in] cloud_tgt the target point cloud dataset
123 * \param[out] transformation_matrix the resultant transformation matrix
124 * \note Uses the weights given by setWeights.
125 */
126 inline void
130
131 /** \brief Estimate a rigid rotation transformation between a source and a target
132 * point cloud using LM. \param[in] cloud_src the source point cloud dataset
133 * \param[in] indices_src the vector of indices describing the points of interest in
134 * \a cloud_src
135 * \param[in] cloud_tgt the target point cloud dataset
136 * \param[out] transformation_matrix the resultant transformation matrix
137 * \note Uses the weights given by setWeights.
138 */
139 inline void
144
145 /** \brief Estimate a rigid rotation transformation between a source and a target
146 * point cloud using LM. \param[in] cloud_src the source point cloud dataset
147 * \param[in] indices_src the vector of indices describing the points of interest in
148 * \a cloud_src
149 * \param[in] cloud_tgt the target point cloud dataset
150 * \param[in] indices_tgt the vector of indices describing the correspondences of the
151 * interest points from \a indices_src
152 * \param[out] transformation_matrix the resultant transformation matrix
153 * \note Uses the weights given by setWeights.
154 */
155 void
161
162 /** \brief Estimate a rigid rotation transformation between a source and a target
163 * point cloud using LM. \param[in] cloud_src the source point cloud dataset
164 * \param[in] cloud_tgt the target point cloud dataset
165 * \param[in] correspondences the vector of correspondences between source and target
166 * point cloud \param[out] transformation_matrix the resultant transformation matrix
167 * \note Uses the weights given by setWeights.
168 */
169 void
172 const pcl::Correspondences& correspondences,
174
175 inline void
176 setWeights(const std::vector<double>& weights)
177 {
178 correspondence_weights_ = weights;
179 }
180
181 /// use the weights given in the pcl::CorrespondencesPtr for one of the
182 /// estimateTransformation (...) methods
183 inline void
188
189 /** \brief Set the function we use to warp points. Defaults to rigid 6D warp.
190 * \param[in] warp_fcn a shared pointer to an object that warps points
191 */
192 void
198
199protected:
201 mutable std::vector<double> correspondence_weights_;
202
203 /** \brief Temporary pointer to the source dataset. */
205
206 /** \brief Temporary pointer to the target dataset. */
208
209 /** \brief Temporary pointer to the source dataset indices. */
211
212 /** \brief Temporary pointer to the target dataset indices. */
214
215 /** \brief The parameterized function used to warp the source to the target. */
218
219 /** Base functor all the models that need non linear optimization must
220 * define their own one and implement operator() (const Eigen::VectorXd& x,
221 * Eigen::VectorXd& fvec) or operator() (const Eigen::VectorXf& x, Eigen::VectorXf&
222 * fvec) depending on the chosen _Scalar
223 */
224 template <typename _Scalar, int NX = Eigen::Dynamic, int NY = Eigen::Dynamic>
225 struct Functor {
228 using InputType = Eigen::Matrix<_Scalar, InputsAtCompileTime, 1>;
229 using ValueType = Eigen::Matrix<_Scalar, ValuesAtCompileTime, 1>;
231 Eigen::Matrix<_Scalar, ValuesAtCompileTime, InputsAtCompileTime>;
232
233 /** \brief Empty Constructor. */
235
236 /** \brief Constructor
237 * \param[in] m_data_points number of data points to evaluate.
238 */
240
241 /** \brief Destructor. */
242 virtual ~Functor() {}
243
244 /** \brief Get the number of values. */
245 int
246 values() const
247 {
248 return (m_data_points_);
249 }
250
251 protected:
253 };
254
255 struct OptimizationFunctor : public Functor<MatScalar> {
257
258 /** Functor constructor
259 * \param[in] m_data_points the number of data points to evaluate
260 * \param[in,out] estimator pointer to the estimator object
261 */
266
267 /** Copy constructor
268 * \param[in] src the optimization functor to copy into this
269 */
272 {
273 *this = src;
274 }
275
276 /** Copy operator
277 * \param[in] src the optimization functor to copy into this
278 */
279 inline OptimizationFunctor&
281 {
283 estimator_ = src.estimator_;
284 return (*this);
285 }
286
287 /** \brief Destructor. */
289
290 /** Fill fvec from x. For the current state vector x fill the f values
291 * \param[in] x state vector
292 * \param[out] fvec f values vector
293 */
294 int
295 operator()(const VectorX& x, VectorX& fvec) const;
296
298 PointTarget,
300 };
301
302 struct OptimizationFunctorWithIndices : public Functor<MatScalar> {
304
305 /** Functor constructor
306 * \param[in] m_data_points the number of data points to evaluate
307 * \param[in,out] estimator pointer to the estimator object
308 */
314
315 /** Copy constructor
316 * \param[in] src the optimization functor to copy into this
317 */
323
324 /** Copy operator
325 * \param[in] src the optimization functor to copy into this
326 */
329 {
331 estimator_ = src.estimator_;
332 return (*this);
333 }
334
335 /** \brief Destructor. */
337
338 /** Fill fvec from x. For the current state vector x fill the f values
339 * \param[in] x state vector
340 * \param[out] fvec f values vector
341 */
342 int
343 operator()(const VectorX& x, VectorX& fvec) const;
344
346 PointTarget,
348 };
349
350public:
352};
353} // namespace registration
354} // namespace pcl
355
356#include <pcl/registration/impl/transformation_estimation_point_to_plane_weighted.hpp>
Iterator class for point clouds with or without given indices.
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.
shared_ptr< TransformationEstimationPointToPlaneWeighted< PointSource, PointTarget, MatScalar > > Ptr
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.
typename TransformationEstimation< PointSource, PointTarget, MatScalar >::Matrix4 Matrix4
pcl::registration::WarpPointRigid< PointSource, PointTarget, MatScalar >::Ptr warp_point_
The parameterized function used to warp the source to the target.
#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.