Point Cloud Library (PCL) 1.15.0
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sac_model_torus.h
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40
41#pragma once
42
44#include <pcl/sample_consensus/model_types.h>
45#include <pcl/sample_consensus/sac_model.h>
46
47namespace pcl {
48
49/** \brief @b SampleConsensusModelTorus defines a model for 3D torus segmentation.
50 * The model coefficients are defined as:
51 * - \b radii.inner : the torus's inner radius
52 * - \b radii.outer : the torus's outer radius
53 * - \b torus_center_point.x : the X coordinate of the center of the torus
54 * - \b torus_center_point.y : the Y coordinate of the center of the torus
55 * - \b torus_center_point.z : the Z coordinate of the center of the torus
56 * - \b torus_normal.x : the X coordinate of the normal of the torus
57 * - \b torus_normal.y : the Y coordinate of the normal of the torus
58 * - \b torus_normal.z : the Z coordinate of the normal of the torus
59 *
60 * \author lasdasdas
61 * \ingroup sample_consensus
62 */
63template <typename PointT, typename PointNT>
65: public SampleConsensusModel<PointT>,
66 public SampleConsensusModelFromNormals<PointT, PointNT> {
75
76 using PointCloud = typename SampleConsensusModel<PointT>::PointCloud;
77 using PointCloudPtr = typename SampleConsensusModel<PointT>::PointCloudPtr;
78 using PointCloudConstPtr = typename SampleConsensusModel<PointT>::PointCloudConstPtr;
79
80public:
81 using Ptr = shared_ptr<SampleConsensusModelTorus<PointT, PointNT>>;
82 using ConstPtr = shared_ptr<const SampleConsensusModelTorus<PointT, PointNT>>;
83
84 /** \brief Constructor for base SampleConsensusModelTorus.
85 * \param[in] cloud the input point cloud dataset
86 * \param[in] random if true set the random seed to the current time, else set to
87 * 12345 (default: false)
88 */
89 SampleConsensusModelTorus(const PointCloudConstPtr& cloud, bool random = false)
90 : SampleConsensusModel<PointT>(cloud, random)
92 {
93 model_name_ = "SampleConsensusModelTorus";
94 sample_size_ = 4;
95 model_size_ = 8;
96 }
97
98 /** \brief Constructor for base SampleConsensusModelTorus.
99 * \param[in] cloud the input point cloud dataset
100 * \param[in] indices a vector of point indices to be used from \a cloud
101 * \param[in] random if true set the random seed to the current time, else set to
102 * 12345 (default: false)
103 */
104 SampleConsensusModelTorus(const PointCloudConstPtr& cloud,
105 const Indices& indices,
106 bool random = false)
107 : SampleConsensusModel<PointT>(cloud, indices, random)
109 {
110 model_name_ = "SampleConsensusModelTorus";
111 sample_size_ = 4;
112 model_size_ = 8;
113 }
114
115 /** \brief Copy constructor.
116 * \param[in] source the model to copy into this
117 */
120 {
121 *this = source;
122 model_name_ = "SampleConsensusModelTorus";
123 }
124
125 /** \brief Empty destructor */
126 ~SampleConsensusModelTorus() override = default;
127
128 /** \brief Copy constructor.
129 * \param[in] source the model to copy into this
130 */
137 /** \brief Check whether the given index samples can form a valid torus model, compute
138 * the model coefficients from these samples and store them in model_coefficients. The
139 * torus coefficients are: radii, torus_center_point, torus_normal.
140 * \param[in] samples the point indices found as possible good candidates for creating a valid model
141 * \param[out] model_coefficients the resultant model coefficients
142 */
143 bool
144 computeModelCoefficients(const Indices& samples,
145 Eigen::VectorXf& model_coefficients) const override;
146
147 /** \brief Compute all distances from the cloud data to a given torus model.
148 * \param[in] model_coefficients the coefficients of a torus model that we need to compute distances to
149 * \param[out] distances the resultant estimated distances
150 */
151 void
152 getDistancesToModel(const Eigen::VectorXf& model_coefficients,
153 std::vector<double>& distances) const override;
154
155 /** \brief Select all the points which respect the given model coefficients as
156 * inliers.
157 * \param[in] model_coefficients the coefficients of a torus model that we need to compute distances to
158 * \param[in] threshold a maximum admissible distance threshold for determining the inliers from the outliers
159 * \param[out] inliers the
160 * resultant model inliers
161 */
162 void
163 selectWithinDistance(const Eigen::VectorXf& model_coefficients,
164 const double threshold,
165 Indices& inliers) override;
166
167 /** \brief Count all the points which respect the given model coefficients as inliers.
168 *
169 * \param[in] model_coefficients the coefficients of a model that we need to compute distances to
170 * \param[in] threshold maximum admissible distance threshold for
171 * determining the inliers from the outliers \return the resultant number of inliers
172 */
173 std::size_t
174 countWithinDistance(const Eigen::VectorXf& model_coefficients,
175 const double threshold) const override;
176
177 /** \brief Recompute the torus coefficients using the given inlier set and return them
178 * to the user.
179 * \param[in] inliers the data inliers found as supporting the model
180 * \param[in] model_coefficients the initial guess for the optimization
181 * \param[out] optimized_coefficients the resultant recomputed coefficients after
182 * non-linear optimization
183 */
184 void
185 optimizeModelCoefficients(const Indices& inliers,
186 const Eigen::VectorXf& model_coefficients,
187 Eigen::VectorXf& optimized_coefficients) const override;
188
189 /** \brief Create a new point cloud with inliers projected onto the torus model.
190 * \param[in] inliers the data inliers that we want to project on the torus model
191 * \param[in] model_coefficients the coefficients of a torus model
192 * \param[out] projected_points the resultant projected points
193 * \param[in] copy_data_fields set to true if we need to copy the other data fields
194 */
195 void
196 projectPoints(const Indices& inliers,
197 const Eigen::VectorXf& model_coefficients,
198 PointCloud& projected_points,
199 bool copy_data_fields = true) const override;
200
201 /** \brief Verify whether a subset of indices verifies the given torus model
202 * coefficients.
203 * \param[in] indices the data indices that need to be tested against the torus model
204 * \param[in] model_coefficients the torus model coefficients
205 * \param[in] threshold a maximum admissible distance threshold for determining the
206 * inliers from the outliers
207 */
208 bool
209 doSamplesVerifyModel(const std::set<index_t>& indices,
210 const Eigen::VectorXf& model_coefficients,
211 const double threshold) const override;
212
213 /** \brief Return a unique id for this model (SACMODEL_TORUS). */
214 inline pcl::SacModel
215 getModelType() const override
216 {
217 return (SACMODEL_TORUS);
218 }
219
220protected:
223
224 /** \brief Project a point onto a torus given by its model coefficients (radii,
225 * torus_center_point, torus_normal)
226 * \param[in] pt the input point to project
227 * \param[in] model_coefficients the coefficients of the torus (radii, torus_center_point, torus_normal)
228 * \param[out] pt_proj the resultant projected point
229 */
230 void
231 projectPointToTorus(const Eigen::Vector3f& pt,
232 const Eigen::Vector3f& pt_n,
233 const Eigen::VectorXf& model_coefficients,
234 Eigen::Vector3f& pt_proj) const;
235
236 /** \brief Check whether a model is valid given the user constraints.
237 * \param[in] model_coefficients the set of model coefficients
238 */
239 bool
240 isModelValid(const Eigen::VectorXf& model_coefficients) const override;
241
242 /** \brief Check if a sample of indices results in a good sample of points
243 * indices. Pure virtual.
244 * \param[in] samples the resultant index samples
245 */
246 bool
247 isSampleGood(const Indices& samples) const override;
248
249private:
250 struct OptimizationFunctor : pcl::Functor<double> {
251 /** Functor constructor
252 * \param[in] indices the indices of data points to evaluate
253 * \param[in] estimator pointer to the estimator object
254 */
255 OptimizationFunctor(const pcl::SampleConsensusModelTorus<PointT, PointNT>* model,
256 const Indices& indices)
257 : pcl::Functor<double>(indices.size()), model_(model), indices_(indices)
258 {}
259
260 /** Cost function to be minimized
261 * \param[in] x the variables array
262 * \param[out] fvec the resultant functions evaluations
263 * \return 0
264 */
265 int
266 operator()(const Eigen::VectorXd& xs, Eigen::VectorXd& fvec) const
267 {
268 // Getting constants from state vector
269 const double& R = xs[0];
270 const double& r = xs[1];
271
272 const double& x0 = xs[2];
273 const double& y0 = xs[3];
274 const double& z0 = xs[4];
275
276 const Eigen::Vector3d centroid{x0, y0, z0};
277
278 const double& nx = xs[5];
279 const double& ny = xs[6];
280 const double& nz = xs[7];
281
282 const Eigen::Vector3d n1{0.0, 0.0, 1.0};
283 const Eigen::Vector3d n2 = Eigen::Vector3d{nx, ny, nz}.normalized();
284
285 for (size_t j = 0; j < indices_.size(); j++) {
286
287 size_t i = indices_[j];
288 const Eigen::Vector3d pt =
289 (*model_->input_)[i].getVector3fMap().template cast<double>();
290
291 Eigen::Vector3d pte{pt - centroid};
292
293 // Transposition is inversion
294 // Using Quaternions instead of Rodrigues
295 pte = Eigen::Quaterniond()
296 .setFromTwoVectors(n1, n2)
297 .toRotationMatrix()
298 .transpose() *
299 pte;
300
301 const double& x = pte[0];
302 const double& y = pte[1];
303 const double& z = pte[2];
304
305 fvec[j] = (std::pow(sqrt(x * x + y * y) - R, 2) + z * z - r * r);
306 }
307 return 0;
308 }
309
311 const Indices& indices_;
312 };
313};
314} // namespace pcl
315
316#ifdef PCL_NO_PRECOMPILE
317#include <pcl/sample_consensus/impl/sac_model_torus.hpp>
318#endif
PointCloud represents the base class in PCL for storing collections of 3D points.
SampleConsensusModelFromNormals represents the base model class for models that require the use of su...
Definition sac_model.h:613
PointCloudNConstPtr normals_
A pointer to the input dataset that contains the point normals of the XYZ dataset.
Definition sac_model.h:671
double normal_distance_weight_
The relative weight (between 0 and 1) to give to the angular distance (0 to pi/2) between point norma...
Definition sac_model.h:666
SampleConsensusModel represents the base model class.
Definition sac_model.h:71
double radius_min_
The minimum and maximum radius limits for the model.
Definition sac_model.h:565
unsigned int sample_size_
The size of a sample from which the model is computed.
Definition sac_model.h:589
typename PointCloud::ConstPtr PointCloudConstPtr
Definition sac_model.h:74
IndicesPtr indices_
A pointer to the vector of point indices to use.
Definition sac_model.h:557
PointCloudConstPtr input_
A boost shared pointer to the point cloud data array.
Definition sac_model.h:554
std::string model_name_
The model name.
Definition sac_model.h:551
unsigned int model_size_
The number of coefficients in the model.
Definition sac_model.h:592
typename PointCloud::Ptr PointCloudPtr
Definition sac_model.h:75
std::vector< double > error_sqr_dists_
A vector holding the distances to the computed model.
Definition sac_model.h:586
SampleConsensusModelTorus defines a model for 3D torus segmentation.
void projectPointToTorus(const Eigen::Vector3f &pt, const Eigen::Vector3f &pt_n, const Eigen::VectorXf &model_coefficients, Eigen::Vector3f &pt_proj) const
Project a point onto a torus given by its model coefficients (radii, torus_center_point,...
SampleConsensusModelTorus(const SampleConsensusModelTorus &source)
Copy constructor.
void selectWithinDistance(const Eigen::VectorXf &model_coefficients, const double threshold, Indices &inliers) override
Select all the points which respect the given model coefficients as inliers.
bool doSamplesVerifyModel(const std::set< index_t > &indices, const Eigen::VectorXf &model_coefficients, const double threshold) const override
Verify whether a subset of indices verifies the given torus model coefficients.
SampleConsensusModelTorus & operator=(const SampleConsensusModelTorus &source)
Copy constructor.
pcl::SacModel getModelType() const override
Return a unique id for this model (SACMODEL_TORUS).
bool isModelValid(const Eigen::VectorXf &model_coefficients) const override
Check whether a model is valid given the user constraints.
bool computeModelCoefficients(const Indices &samples, Eigen::VectorXf &model_coefficients) const override
Check whether the given index samples can form a valid torus model, compute the model coefficients fr...
shared_ptr< SampleConsensusModelTorus< PointT, PointNT > > Ptr
void optimizeModelCoefficients(const Indices &inliers, const Eigen::VectorXf &model_coefficients, Eigen::VectorXf &optimized_coefficients) const override
Recompute the torus coefficients using the given inlier set and return them to the user.
~SampleConsensusModelTorus() override=default
Empty destructor.
std::size_t countWithinDistance(const Eigen::VectorXf &model_coefficients, const double threshold) const override
Count all the points which respect the given model coefficients as inliers.
shared_ptr< const SampleConsensusModelTorus< PointT, PointNT > > ConstPtr
SampleConsensusModelTorus(const PointCloudConstPtr &cloud, const Indices &indices, bool random=false)
Constructor for base SampleConsensusModelTorus.
SampleConsensusModelTorus(const PointCloudConstPtr &cloud, bool random=false)
Constructor for base SampleConsensusModelTorus.
bool isSampleGood(const Indices &samples) const override
Check if a sample of indices results in a good sample of points indices.
void getDistancesToModel(const Eigen::VectorXf &model_coefficients, std::vector< double > &distances) const override
Compute all distances from the cloud data to a given torus model.
void projectPoints(const Indices &inliers, const Eigen::VectorXf &model_coefficients, PointCloud &projected_points, bool copy_data_fields=true) const override
Create a new point cloud with inliers projected onto the torus model.
Define standard C methods to do distance calculations.
@ SACMODEL_TORUS
Definition model_types.h:54
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition types.h:133
Base functor all the models that need non linear optimization must define their own one and implement...
Definition sac_model.h:680
A point structure representing Euclidean xyz coordinates, and the RGB color.