Point Cloud Library (PCL) 1.12.0
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sac_model_line.hpp
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40
41#ifndef PCL_SAMPLE_CONSENSUS_IMPL_SAC_MODEL_LINE_H_
42#define PCL_SAMPLE_CONSENSUS_IMPL_SAC_MODEL_LINE_H_
43
44#include <pcl/sample_consensus/sac_model_line.h>
45#include <pcl/common/centroid.h>
46#include <pcl/common/concatenate.h>
47#include <pcl/common/eigen.h> // for eigen33
48
49//////////////////////////////////////////////////////////////////////////
50template <typename PointT> bool
52{
53 if (samples.size () != sample_size_)
54 {
55 PCL_ERROR ("[pcl::SampleConsensusModelLine::isSampleGood] Wrong number of samples (is %lu, should be %lu)!\n", samples.size (), sample_size_);
56 return (false);
57 }
58 // Make sure that the two sample points are not identical
59 if (
60 ((*input_)[samples[0]].x != (*input_)[samples[1]].x)
61 ||
62 ((*input_)[samples[0]].y != (*input_)[samples[1]].y)
63 ||
64 ((*input_)[samples[0]].z != (*input_)[samples[1]].z))
65 {
66 return (true);
67 }
68
69 return (false);
70}
71
72//////////////////////////////////////////////////////////////////////////
73template <typename PointT> bool
75 const Indices &samples, Eigen::VectorXf &model_coefficients) const
76{
77 // Need 2 samples
78 if (samples.size () != sample_size_)
79 {
80 PCL_ERROR ("[pcl::SampleConsensusModelLine::computeModelCoefficients] Invalid set of samples given (%lu)!\n", samples.size ());
81 return (false);
82 }
83
84 if (std::abs ((*input_)[samples[0]].x - (*input_)[samples[1]].x) <= std::numeric_limits<float>::epsilon () &&
85 std::abs ((*input_)[samples[0]].y - (*input_)[samples[1]].y) <= std::numeric_limits<float>::epsilon () &&
86 std::abs ((*input_)[samples[0]].z - (*input_)[samples[1]].z) <= std::numeric_limits<float>::epsilon ())
87 {
88 return (false);
89 }
90
91 model_coefficients.resize (model_size_);
92 model_coefficients[0] = (*input_)[samples[0]].x;
93 model_coefficients[1] = (*input_)[samples[0]].y;
94 model_coefficients[2] = (*input_)[samples[0]].z;
95
96 model_coefficients[3] = (*input_)[samples[1]].x - model_coefficients[0];
97 model_coefficients[4] = (*input_)[samples[1]].y - model_coefficients[1];
98 model_coefficients[5] = (*input_)[samples[1]].z - model_coefficients[2];
99
100 model_coefficients.template tail<3> ().normalize ();
101 PCL_DEBUG ("[pcl::SampleConsensusModelLine::computeModelCoefficients] Model is (%g,%g,%g,%g,%g,%g).\n",
102 model_coefficients[0], model_coefficients[1], model_coefficients[2],
103 model_coefficients[3], model_coefficients[4], model_coefficients[5]);
104 return (true);
105}
106
107//////////////////////////////////////////////////////////////////////////
108template <typename PointT> void
110 const Eigen::VectorXf &model_coefficients, std::vector<double> &distances) const
111{
112 // Needs a valid set of model coefficients
113 if (!isModelValid (model_coefficients))
114 {
115 return;
116 }
117
118 distances.resize (indices_->size ());
119
120 // Obtain the line point and direction
121 Eigen::Vector4f line_pt (model_coefficients[0], model_coefficients[1], model_coefficients[2], 0);
122 Eigen::Vector4f line_dir (model_coefficients[3], model_coefficients[4], model_coefficients[5], 0);
123 line_dir.normalize ();
124
125 // Iterate through the 3d points and calculate the distances from them to the line
126 for (std::size_t i = 0; i < indices_->size (); ++i)
127 {
128 // Calculate the distance from the point to the line
129 // D = ||(P2-P1) x (P1-P0)|| / ||P2-P1|| = norm (cross (p2-p1, p2-p0)) / norm(p2-p1)
130 // Need to estimate sqrt here to keep MSAC and friends general
131 distances[i] = sqrt ((line_pt - (*input_)[(*indices_)[i]].getVector4fMap ()).cross3 (line_dir).squaredNorm ());
132 }
133}
134
135//////////////////////////////////////////////////////////////////////////
136template <typename PointT> void
138 const Eigen::VectorXf &model_coefficients, const double threshold, Indices &inliers)
139{
140 // Needs a valid set of model coefficients
141 if (!isModelValid (model_coefficients))
142 return;
143
144 double sqr_threshold = threshold * threshold;
145
146 inliers.clear ();
147 error_sqr_dists_.clear ();
148 inliers.reserve (indices_->size ());
149 error_sqr_dists_.reserve (indices_->size ());
150
151 // Obtain the line point and direction
152 Eigen::Vector4f line_pt (model_coefficients[0], model_coefficients[1], model_coefficients[2], 0);
153 Eigen::Vector4f line_dir (model_coefficients[3], model_coefficients[4], model_coefficients[5], 0);
154 line_dir.normalize ();
155
156 // Iterate through the 3d points and calculate the distances from them to the line
157 for (std::size_t i = 0; i < indices_->size (); ++i)
158 {
159 // Calculate the distance from the point to the line
160 // D = ||(P2-P1) x (P1-P0)|| / ||P2-P1|| = norm (cross (p2-p1, p2-p0)) / norm(p2-p1)
161 double sqr_distance = (line_pt - (*input_)[(*indices_)[i]].getVector4fMap ()).cross3 (line_dir).squaredNorm ();
162
163 if (sqr_distance < sqr_threshold)
164 {
165 // Returns the indices of the points whose squared distances are smaller than the threshold
166 inliers.push_back ((*indices_)[i]);
167 error_sqr_dists_.push_back (sqr_distance);
168 }
169 }
170}
171
172//////////////////////////////////////////////////////////////////////////
173template <typename PointT> std::size_t
175 const Eigen::VectorXf &model_coefficients, const double threshold) const
176{
177 // Needs a valid set of model coefficients
178 if (!isModelValid (model_coefficients))
179 return (0);
180
181 double sqr_threshold = threshold * threshold;
182
183 std::size_t nr_p = 0;
184
185 // Obtain the line point and direction
186 Eigen::Vector4f line_pt (model_coefficients[0], model_coefficients[1], model_coefficients[2], 0.0f);
187 Eigen::Vector4f line_dir (model_coefficients[3], model_coefficients[4], model_coefficients[5], 0.0f);
188 line_dir.normalize ();
189
190 // Iterate through the 3d points and calculate the distances from them to the line
191 for (std::size_t i = 0; i < indices_->size (); ++i)
192 {
193 // Calculate the distance from the point to the line
194 // D = ||(P2-P1) x (P1-P0)|| / ||P2-P1|| = norm (cross (p2-p1, p2-p0)) / norm(p2-p1)
195 double sqr_distance = (line_pt - (*input_)[(*indices_)[i]].getVector4fMap ()).cross3 (line_dir).squaredNorm ();
196
197 if (sqr_distance < sqr_threshold)
198 nr_p++;
199 }
200 return (nr_p);
201}
202
203//////////////////////////////////////////////////////////////////////////
204template <typename PointT> void
206 const Indices &inliers, const Eigen::VectorXf &model_coefficients, Eigen::VectorXf &optimized_coefficients) const
207{
208 // Needs a valid set of model coefficients
209 if (!isModelValid (model_coefficients))
210 {
211 optimized_coefficients = model_coefficients;
212 return;
213 }
214
215 // Need more than the minimum sample size to make a difference
216 if (inliers.size () <= sample_size_)
217 {
218 PCL_ERROR ("[pcl::SampleConsensusModelLine::optimizeModelCoefficients] Not enough inliers to refine/optimize the model's coefficients (%lu)! Returning the same coefficients.\n", inliers.size ());
219 optimized_coefficients = model_coefficients;
220 return;
221 }
222
223 optimized_coefficients.resize (model_size_);
224
225 // Compute the 3x3 covariance matrix
226 Eigen::Vector4f centroid;
227 if (0 == compute3DCentroid (*input_, inliers, centroid))
228 {
229 PCL_WARN ("[pcl::SampleConsensusModelLine::optimizeModelCoefficients] compute3DCentroid failed (returned 0) because there are no valid inliers.\n");
230 optimized_coefficients = model_coefficients;
231 return;
232 }
233 Eigen::Matrix3f covariance_matrix;
234 computeCovarianceMatrix (*input_, inliers, centroid, covariance_matrix);
235 optimized_coefficients[0] = centroid[0];
236 optimized_coefficients[1] = centroid[1];
237 optimized_coefficients[2] = centroid[2];
238
239 // Extract the eigenvalues and eigenvectors
240 EIGEN_ALIGN16 Eigen::Vector3f eigen_values;
241 EIGEN_ALIGN16 Eigen::Vector3f eigen_vector;
242 pcl::eigen33 (covariance_matrix, eigen_values);
243 pcl::computeCorrespondingEigenVector (covariance_matrix, eigen_values [2], eigen_vector);
244 //pcl::eigen33 (covariance_matrix, eigen_vectors, eigen_values);
245
246 optimized_coefficients.template tail<3> ().matrix () = eigen_vector;
247}
248
249//////////////////////////////////////////////////////////////////////////
250template <typename PointT> void
252 const Indices &inliers, const Eigen::VectorXf &model_coefficients, PointCloud &projected_points, bool copy_data_fields) const
253{
254 // Needs a valid model coefficients
255 if (!isModelValid (model_coefficients))
256 return;
257
258 // Obtain the line point and direction
259 Eigen::Vector4f line_pt (model_coefficients[0], model_coefficients[1], model_coefficients[2], 0.0f);
260 Eigen::Vector4f line_dir (model_coefficients[3], model_coefficients[4], model_coefficients[5], 0.0f);
261
262 projected_points.header = input_->header;
263 projected_points.is_dense = input_->is_dense;
264
265 // Copy all the data fields from the input cloud to the projected one?
266 if (copy_data_fields)
267 {
268 // Allocate enough space and copy the basics
269 projected_points.resize (input_->size ());
270 projected_points.width = input_->width;
271 projected_points.height = input_->height;
272
273 using FieldList = typename pcl::traits::fieldList<PointT>::type;
274 // Iterate over each point
275 for (std::size_t i = 0; i < projected_points.size (); ++i)
276 // Iterate over each dimension
277 pcl::for_each_type <FieldList> (NdConcatenateFunctor <PointT, PointT> ((*input_)[i], projected_points[i]));
278
279 // Iterate through the 3d points and calculate the distances from them to the line
280 for (const auto &inlier : inliers)
281 {
282 Eigen::Vector4f pt ((*input_)[inlier].x, (*input_)[inlier].y, (*input_)[inlier].z, 0.0f);
283 // double k = (DOT_PROD_3D (points[i], p21) - dotA_B) / dotB_B;
284 float k = (pt.dot (line_dir) - line_pt.dot (line_dir)) / line_dir.dot (line_dir);
285
286 Eigen::Vector4f pp = line_pt + k * line_dir;
287 // Calculate the projection of the point on the line (pointProj = A + k * B)
288 projected_points[inlier].x = pp[0];
289 projected_points[inlier].y = pp[1];
290 projected_points[inlier].z = pp[2];
291 }
292 }
293 else
294 {
295 // Allocate enough space and copy the basics
296 projected_points.resize (inliers.size ());
297 projected_points.width = inliers.size ();
298 projected_points.height = 1;
299
300 using FieldList = typename pcl::traits::fieldList<PointT>::type;
301 // Iterate over each point
302 for (std::size_t i = 0; i < inliers.size (); ++i)
303 // Iterate over each dimension
304 pcl::for_each_type <FieldList> (NdConcatenateFunctor <PointT, PointT> ((*input_)[inliers[i]], projected_points[i]));
305
306 // Iterate through the 3d points and calculate the distances from them to the line
307 for (std::size_t i = 0; i < inliers.size (); ++i)
308 {
309 Eigen::Vector4f pt ((*input_)[inliers[i]].x, (*input_)[inliers[i]].y, (*input_)[inliers[i]].z, 0.0f);
310 // double k = (DOT_PROD_3D (points[i], p21) - dotA_B) / dotB_B;
311 float k = (pt.dot (line_dir) - line_pt.dot (line_dir)) / line_dir.dot (line_dir);
312
313 Eigen::Vector4f pp = line_pt + k * line_dir;
314 // Calculate the projection of the point on the line (pointProj = A + k * B)
315 projected_points[i].x = pp[0];
316 projected_points[i].y = pp[1];
317 projected_points[i].z = pp[2];
318 }
319 }
320}
321
322//////////////////////////////////////////////////////////////////////////
323template <typename PointT> bool
325 const std::set<index_t> &indices, const Eigen::VectorXf &model_coefficients, const double threshold) const
326{
327 // Needs a valid set of model coefficients
328 if (!isModelValid (model_coefficients))
329 return (false);
330
331 // Obtain the line point and direction
332 Eigen::Vector4f line_pt (model_coefficients[0], model_coefficients[1], model_coefficients[2], 0.0f);
333 Eigen::Vector4f line_dir (model_coefficients[3], model_coefficients[4], model_coefficients[5], 0.0f);
334 line_dir.normalize ();
335
336 double sqr_threshold = threshold * threshold;
337 // Iterate through the 3d points and calculate the distances from them to the line
338 for (const auto &index : indices)
339 {
340 // Calculate the distance from the point to the line
341 // D = ||(P2-P1) x (P1-P0)|| / ||P2-P1|| = norm (cross (p2-p1, p2-p0)) / norm(p2-p1)
342 if ((line_pt - (*input_)[index].getVector4fMap ()).cross3 (line_dir).squaredNorm () > sqr_threshold)
343 return (false);
344 }
345
346 return (true);
347}
348
349#define PCL_INSTANTIATE_SampleConsensusModelLine(T) template class PCL_EXPORTS pcl::SampleConsensusModelLine<T>;
350
351#endif // PCL_SAMPLE_CONSENSUS_IMPL_SAC_MODEL_LINE_H_
352
Define methods for centroid estimation and covariance matrix calculus.
bool computeModelCoefficients(const Indices &samples, Eigen::VectorXf &model_coefficients) const override
Check whether the given index samples can form a valid line model, compute the model coefficients fro...
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.
void getDistancesToModel(const Eigen::VectorXf &model_coefficients, std::vector< double > &distances) const override
Compute all squared distances from the cloud data to a given line model.
bool isSampleGood(const Indices &samples) const override
Check if a sample of indices results in a good sample of points indices.
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 line model coefficients.
void optimizeModelCoefficients(const Indices &inliers, const Eigen::VectorXf &model_coefficients, Eigen::VectorXf &optimized_coefficients) const override
Recompute the line coefficients using the given inlier set and return them to the user.
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 line model.
typename SampleConsensusModel< PointT >::PointCloud PointCloud
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.
void computeCorrespondingEigenVector(const Matrix &mat, const typename Matrix::Scalar &eigenvalue, Vector &eigenvector)
determines the corresponding eigenvector to the given eigenvalue of the symmetric positive semi defin...
Definition eigen.hpp:226
unsigned int computeCovarianceMatrix(const pcl::PointCloud< PointT > &cloud, const Eigen::Matrix< Scalar, 4, 1 > &centroid, Eigen::Matrix< Scalar, 3, 3 > &covariance_matrix)
Compute the 3x3 covariance matrix of a given set of points.
Definition centroid.hpp:180
void eigen33(const Matrix &mat, typename Matrix::Scalar &eigenvalue, Vector &eigenvector)
determines the eigenvector and eigenvalue of the smallest eigenvalue of the symmetric positive semi d...
Definition eigen.hpp:296
unsigned int compute3DCentroid(ConstCloudIterator< PointT > &cloud_iterator, Eigen::Matrix< Scalar, 4, 1 > &centroid)
Compute the 3D (X-Y-Z) centroid of a set of points and return it as a 3D vector.
Definition centroid.hpp:56
void for_each_type(F f)
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition types.h:133