Point Cloud Library (PCL) 1.12.1
transformation_estimation_lm.h
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40
41#pragma once
42
43#include <pcl/registration/transformation_estimation.h>
44#include <pcl/registration/warp_point_rigid.h>
45#include <pcl/memory.h>
46
47namespace pcl {
48namespace registration {
49/** @b TransformationEstimationLM implements Levenberg Marquardt-based
50 * estimation of the transformation aligning the given correspondences.
51 *
52 * \note The class is templated on the source and target point types as well as on the
53 * output scalar of the transformation matrix (i.e., float or double). Default: float.
54 * \author Radu B. Rusu
55 * \ingroup registration
56 */
57template <typename PointSource, typename PointTarget, typename MatScalar = float>
59: public TransformationEstimation<PointSource, PointTarget, MatScalar> {
61 using PointCloudSourcePtr = typename PointCloudSource::Ptr;
62 using PointCloudSourceConstPtr = typename PointCloudSource::ConstPtr;
63
65
66 using PointIndicesPtr = PointIndices::Ptr;
67 using PointIndicesConstPtr = PointIndices::ConstPtr;
68
69public:
70 using Ptr =
71 shared_ptr<TransformationEstimationLM<PointSource, PointTarget, MatScalar>>;
72 using ConstPtr =
73 shared_ptr<const TransformationEstimationLM<PointSource, PointTarget, MatScalar>>;
74
75 using VectorX = Eigen::Matrix<MatScalar, Eigen::Dynamic, 1>;
76 using Vector4 = Eigen::Matrix<MatScalar, 4, 1>;
77 using Matrix4 =
79
80 /** \brief Constructor. */
82
83 /** \brief Copy constructor.
84 * \param[in] src the TransformationEstimationLM object to copy into this
85 */
87 : tmp_src_(src.tmp_src_)
88 , tmp_tgt_(src.tmp_tgt_)
91 , warp_point_(src.warp_point_){};
92
93 /** \brief Copy operator.
94 * \param[in] src the TransformationEstimationLM object to copy into this
95 */
98 {
99 tmp_src_ = src.tmp_src_;
100 tmp_tgt_ = src.tmp_tgt_;
104 }
105
106 /** \brief Destructor. */
108
109 /** \brief Estimate a rigid rotation transformation between a source and a target
110 * point cloud using LM. \param[in] cloud_src the source point cloud dataset
111 * \param[in] cloud_tgt the target point cloud dataset
112 * \param[out] transformation_matrix the resultant transformation matrix
113 */
114 inline void
116 const pcl::PointCloud<PointTarget>& cloud_tgt,
117 Matrix4& transformation_matrix) const override;
118
119 /** \brief Estimate a rigid rotation transformation between a source and a target
120 * point cloud using LM. \param[in] cloud_src the source point cloud dataset
121 * \param[in] indices_src the vector of indices describing the points of interest in
122 * \a cloud_src
123 * \param[in] cloud_tgt the target point cloud dataset
124 * \param[out] transformation_matrix the resultant transformation matrix
125 */
126 inline void
128 const pcl::Indices& indices_src,
129 const pcl::PointCloud<PointTarget>& cloud_tgt,
130 Matrix4& transformation_matrix) const override;
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[in] indices_tgt the vector of indices describing the correspondences of the
138 * interest points from \a indices_src
139 * \param[out] transformation_matrix the resultant transformation matrix
140 */
141 inline void
143 const pcl::Indices& indices_src,
144 const pcl::PointCloud<PointTarget>& cloud_tgt,
145 const pcl::Indices& indices_tgt,
146 Matrix4& transformation_matrix) const override;
147
148 /** \brief Estimate a rigid rotation transformation between a source and a target
149 * point cloud using LM. \param[in] cloud_src the source point cloud dataset
150 * \param[in] cloud_tgt the target point cloud dataset
151 * \param[in] correspondences the vector of correspondences between source and target
152 * point cloud \param[out] transformation_matrix the resultant transformation matrix
153 */
154 inline void
156 const pcl::PointCloud<PointTarget>& cloud_tgt,
157 const pcl::Correspondences& correspondences,
158 Matrix4& transformation_matrix) const override;
159
160 /** \brief Set the function we use to warp points. Defaults to rigid 6D warp.
161 * \param[in] warp_fcn a shared pointer to an object that warps points
162 */
163 void
166 {
167 warp_point_ = warp_fcn;
168 }
169
170protected:
171 /** \brief Compute the distance between a source point and its corresponding target
172 * point \param[in] p_src The source point \param[in] p_tgt The target point \return
173 * The distance between \a p_src and \a p_tgt
174 *
175 * \note Older versions of PCL used this method internally for calculating the
176 * optimization gradient. Since PCL 1.7, a switch has been made to the
177 * computeDistance method using Vector4 types instead. This method is only
178 * kept for API compatibility reasons.
179 */
180 virtual MatScalar
181 computeDistance(const PointSource& p_src, const PointTarget& p_tgt) const
182 {
183 Vector4 s(p_src.x, p_src.y, p_src.z, 0);
184 Vector4 t(p_tgt.x, p_tgt.y, p_tgt.z, 0);
185 return ((s - t).norm());
186 }
187
188 /** \brief Compute the distance between a source point and its corresponding target
189 * point \param[in] p_src The source point \param[in] p_tgt The target point \return
190 * The distance between \a p_src and \a p_tgt
191 *
192 * \note A different distance function can be defined by creating a subclass of
193 * TransformationEstimationLM and overriding this method.
194 * (See \a TransformationEstimationPointToPlane)
195 */
196 virtual MatScalar
197 computeDistance(const Vector4& p_src, const PointTarget& p_tgt) const
198 {
199 Vector4 t(p_tgt.x, p_tgt.y, p_tgt.z, 0);
200 return ((p_src - t).norm());
201 }
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 {
226 using Scalar = _Scalar;
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 */
239 Functor(int m_data_points) : m_data_points_(m_data_points) {}
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> {
256 using Functor<MatScalar>::values;
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 */
262 OptimizationFunctor(int m_data_points, const TransformationEstimationLM* estimator)
263 : Functor<MatScalar>(m_data_points), estimator_(estimator)
264 {}
265
266 /** Copy constructor
267 * \param[in] src the optimization functor to copy into this
268 */
270 : Functor<MatScalar>(src.m_data_points_), estimator_()
271 {
272 *this = src;
273 }
274
275 /** Copy operator
276 * \param[in] src the optimization functor to copy into this
277 */
278 inline OptimizationFunctor&
280 {
283 return (*this);
284 }
285
286 /** \brief Destructor. */
288
289 /** Fill fvec from x. For the current state vector x fill the f values
290 * \param[in] x state vector
291 * \param[out] fvec f values vector
292 */
293 int
294 operator()(const VectorX& x, VectorX& fvec) const;
295
297 };
298
299 struct OptimizationFunctorWithIndices : public Functor<MatScalar> {
300 using Functor<MatScalar>::values;
301
302 /** Functor constructor
303 * \param[in] m_data_points the number of data points to evaluate
304 * \param[in,out] estimator pointer to the estimator object
305 */
307 const TransformationEstimationLM* estimator)
308 : Functor<MatScalar>(m_data_points), estimator_(estimator)
309 {}
310
311 /** Copy constructor
312 * \param[in] src the optimization functor to copy into this
313 */
315 : Functor<MatScalar>(src.m_data_points_), estimator_()
316 {
317 *this = src;
318 }
319
320 /** Copy operator
321 * \param[in] src the optimization functor to copy into this
322 */
325 {
328 return (*this);
329 }
330
331 /** \brief Destructor. */
333
334 /** Fill fvec from x. For the current state vector x fill the f values
335 * \param[in] x state vector
336 * \param[out] fvec f values vector
337 */
338 int
339 operator()(const VectorX& x, VectorX& fvec) const;
340
342 };
343
344public:
346};
347} // namespace registration
348} // namespace pcl
349
350#include <pcl/registration/impl/transformation_estimation_lm.hpp>
shared_ptr< PointCloud< PointSource > > Ptr
Definition: point_cloud.h:413
shared_ptr< const PointCloud< PointSource > > ConstPtr
Definition: point_cloud.h:414
TransformationEstimation represents the base class for methods for transformation estimation based on...
TransformationEstimationLM implements Levenberg Marquardt-based estimation of the transformation alig...
TransformationEstimationLM & operator=(const TransformationEstimationLM &src)
Copy operator.
TransformationEstimationLM(const TransformationEstimationLM &src)
Copy constructor.
shared_ptr< const TransformationEstimationLM< PointSource, PointTarget, MatScalar > > ConstPtr
virtual MatScalar computeDistance(const PointSource &p_src, const PointTarget &p_tgt) const
Compute the distance between a source point and its corresponding target point.
shared_ptr< TransformationEstimationLM< PointSource, PointTarget, MatScalar > > Ptr
void estimateRigidTransformation(const pcl::PointCloud< PointSource > &cloud_src, const pcl::PointCloud< PointTarget > &cloud_tgt, Matrix4 &transformation_matrix) const override
Estimate a rigid rotation transformation between a source and a target point cloud using LM.
virtual MatScalar computeDistance(const Vector4 &p_src, const PointTarget &p_tgt) const
Compute the distance between a source point and its corresponding target point.
Eigen::Matrix< MatScalar, Eigen::Dynamic, 1 > VectorX
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.
const PointCloudSource * tmp_src_
Temporary pointer to the source dataset.
typename TransformationEstimation< PointSource, PointTarget, MatScalar >::Matrix4 Matrix4
const PointCloudTarget * tmp_tgt_
Temporary pointer to the target dataset.
pcl::registration::WarpPointRigid< PointSource, PointTarget, MatScalar >::Ptr warp_point_
The parameterized function used to warp the source to the target.
void setWarpFunction(const typename WarpPointRigid< PointSource, PointTarget, MatScalar >::Ptr &warp_fcn)
Set the function we use to warp points.
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
shared_ptr< ::pcl::PointIndices > Ptr
Definition: PointIndices.h:13
shared_ptr< const ::pcl::PointIndices > ConstPtr
Definition: PointIndices.h:14
Base functor all the models that need non linear optimization must define their own one and implement...
Eigen::Matrix< _Scalar, InputsAtCompileTime, 1 > InputType
Eigen::Matrix< _Scalar, ValuesAtCompileTime, 1 > ValueType
Eigen::Matrix< _Scalar, ValuesAtCompileTime, InputsAtCompileTime > JacobianType
OptimizationFunctor(int m_data_points, const TransformationEstimationLM *estimator)
Functor constructor.
int operator()(const VectorX &x, VectorX &fvec) const
Fill fvec from x.
OptimizationFunctor & operator=(const OptimizationFunctor &src)
Copy operator.
const TransformationEstimationLM< PointSource, PointTarget, MatScalar > * estimator_
OptimizationFunctorWithIndices(const OptimizationFunctorWithIndices &src)
Copy constructor.
OptimizationFunctorWithIndices(int m_data_points, const TransformationEstimationLM *estimator)
Functor constructor.
const TransformationEstimationLM< PointSource, PointTarget, MatScalar > * estimator_
OptimizationFunctorWithIndices & operator=(const OptimizationFunctorWithIndices &src)
Copy operator.