Point Cloud Library (PCL)  1.14.1
transformation_estimation_point_to_plane_weighted.h
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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 
47 namespace pcl {
48 namespace 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  */
58 template <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 
70 public:
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  */
93  : tmp_src_(src.tmp_src_)
94  , tmp_tgt_(src.tmp_tgt_)
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_;
112  warp_point_ = src.warp_point_;
115  return (*this);
116  }
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 
200 protected:
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>;
231  using JacobianType =
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> {
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  {
284  estimator_ = src.estimator_;
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> {
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  {
332  estimator_ = src.estimator_;
333  return (*this);
334  }
335 
336  /** \brief Destructor. */
337  virtual ~OptimizationFunctorWithIndices() = default;
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 
351 public:
353 };
354 } // namespace registration
355 } // namespace pcl
356 
357 #include <pcl/registration/impl/transformation_estimation_point_to_plane_weighted.hpp>
const PointCloudTarget * tmp_tgt_
Temporary pointer to the target dataset.
shared_ptr< PointCloud< PointSource > > Ptr
Definition: point_cloud.h:413
const pcl::Indices * tmp_idx_src_
Temporary pointer to the source dataset indices.
typename TransformationEstimation< PointSource, PointTarget, MatScalar >::Matrix4 Matrix4
void setWarpFunction(const typename WarpPointRigid< PointSource, PointTarget, MatScalar >::Ptr &warp_fcn)
Set the function we use to warp points.
shared_ptr< const TransformationEstimationLM< PointSource, PointTarget, MatScalar >> ConstPtr
void setUseCorrespondenceWeights(bool use_correspondence_weights)
use the weights given in the pcl::CorrespondencesPtr for one of the estimateTransformation (...
#define PCL_MAKE_ALIGNED_OPERATOR_NEW
Macro to signal a class requires a custom allocator.
Definition: memory.h:63
const pcl::Indices * tmp_idx_tgt_
Temporary pointer to the target dataset indices.
shared_ptr< ::pcl::PointIndices > Ptr
Definition: PointIndices.h:13
OptimizationFunctorWithIndices & operator=(const OptimizationFunctorWithIndices &src)
Copy operator.
const PointCloudSource * tmp_src_
Temporary pointer to the source dataset.
Base functor all the models that need non linear optimization must define their own one and implement...
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.
TransformationEstimationPointToPlaneWeighted uses Levenberg Marquardt optimization to find the transf...
shared_ptr< WarpPointRigid< PointSourceT, PointTargetT, Scalar >> Ptr
OptimizationFunctor(int m_data_points, const TransformationEstimationPointToPlaneWeighted *estimator)
Functor constructor.
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition: types.h:133
TransformationEstimationPointToPlaneWeighted & operator=(const TransformationEstimationPointToPlaneWeighted &src)
Copy operator.
shared_ptr< TransformationEstimationPointToPlaneWeighted< PointSource, PointTarget, MatScalar >> Ptr
shared_ptr< const ::pcl::PointIndices > ConstPtr
Definition: PointIndices.h:14
TransformationEstimationPointToPlaneWeighted(const TransformationEstimationPointToPlaneWeighted &src)
Copy constructor.
shared_ptr< const PointCloud< PointSource > > ConstPtr
Definition: point_cloud.h:414
TransformationEstimationPointToPlane uses Levenberg Marquardt optimization to find the transformation...
Eigen::Matrix< MatScalar, Eigen::Dynamic, 1 > VectorX
std::vector< pcl::Correspondence, Eigen::aligned_allocator< pcl::Correspondence > > Correspondences
OptimizationFunctorWithIndices(int m_data_points, const TransformationEstimationPointToPlaneWeighted *estimator)
Functor constructor.
Eigen::Matrix< MatScalar, ValuesAtCompileTime, InputsAtCompileTime > JacobianType
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...
TransformationEstimation represents the base class for methods for transformation estimation based on...
const TransformationEstimationPointToPlaneWeighted< PointSource, PointTarget, MatScalar > * estimator_
const TransformationEstimationPointToPlaneWeighted< PointSource, PointTarget, MatScalar > * estimator_
virtual ~TransformationEstimationPointToPlaneWeighted()=default
Destructor.