Point Cloud Library (PCL)  1.12.1-dev
sac_model_stick.hpp
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40 
41 #ifndef PCL_SAMPLE_CONSENSUS_IMPL_SAC_MODEL_STICK_H_
42 #define PCL_SAMPLE_CONSENSUS_IMPL_SAC_MODEL_STICK_H_
43 
44 #include <pcl/sample_consensus/sac_model_stick.h>
45 #include <pcl/common/centroid.h>
46 #include <pcl/common/concatenate.h>
47 #include <pcl/common/eigen.h> // for eigen33
48 
49 //////////////////////////////////////////////////////////////////////////
50 template <typename PointT> bool
52 {
53  if (samples.size () != sample_size_)
54  {
55  PCL_ERROR ("[pcl::SampleConsensusModelStick::isSampleGood] Wrong number of samples (is %lu, should be %lu)!\n", samples.size (), sample_size_);
56  return (false);
57  }
58  if (
59  ((*input_)[samples[0]].x != (*input_)[samples[1]].x)
60  &&
61  ((*input_)[samples[0]].y != (*input_)[samples[1]].y)
62  &&
63  ((*input_)[samples[0]].z != (*input_)[samples[1]].z))
64  {
65  return (true);
66  }
67 
68  return (false);
69 }
70 
71 //////////////////////////////////////////////////////////////////////////
72 template <typename PointT> bool
74  const Indices &samples, Eigen::VectorXf &model_coefficients) const
75 {
76  // Need 2 samples
77  if (samples.size () != sample_size_)
78  {
79  PCL_ERROR ("[pcl::SampleConsensusModelStick::computeModelCoefficients] Invalid set of samples given (%lu)!\n", samples.size ());
80  return (false);
81  }
82 
83  model_coefficients.resize (model_size_);
84  model_coefficients[0] = (*input_)[samples[0]].x;
85  model_coefficients[1] = (*input_)[samples[0]].y;
86  model_coefficients[2] = (*input_)[samples[0]].z;
87 
88  model_coefficients[3] = (*input_)[samples[1]].x;
89  model_coefficients[4] = (*input_)[samples[1]].y;
90  model_coefficients[5] = (*input_)[samples[1]].z;
91 
92 // model_coefficients[3] = (*input_)[samples[1]].x - model_coefficients[0];
93 // model_coefficients[4] = (*input_)[samples[1]].y - model_coefficients[1];
94 // model_coefficients[5] = (*input_)[samples[1]].z - model_coefficients[2];
95 
96 // model_coefficients.template segment<3> (3).normalize ();
97  // We don't care about model_coefficients[6] which is the width (radius) of the stick
98 
99  PCL_DEBUG ("[pcl::SampleConsensusModelStick::computeModelCoefficients] Model is (%g,%g,%g,%g,%g,%g).\n",
100  model_coefficients[0], model_coefficients[1], model_coefficients[2],
101  model_coefficients[3], model_coefficients[4], model_coefficients[5]);
102  return (true);
103 }
104 
105 //////////////////////////////////////////////////////////////////////////
106 template <typename PointT> void
108  const Eigen::VectorXf &model_coefficients, std::vector<double> &distances) const
109 {
110  // Needs a valid set of model coefficients
111  if (!isModelValid (model_coefficients))
112  {
113  PCL_ERROR ("[pcl::SampleConsensusModelStick::getDistancesToModel] Given model is invalid!\n");
114  return;
115  }
116 
117  float sqr_threshold = static_cast<float> (radius_max_ * radius_max_);
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], 0.0f);
122  Eigen::Vector4f line_dir (model_coefficients[3], model_coefficients[4], model_coefficients[5], 0.0f);
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  float sqr_distance = (line_pt - (*input_)[(*indices_)[i]].getVector4fMap ()).cross3 (line_dir).squaredNorm ();
131 
132  if (sqr_distance < sqr_threshold)
133  {
134  // Need to estimate sqrt here to keep MSAC and friends general
135  distances[i] = std::sqrt (sqr_distance);
136  }
137  else
138  {
139  // Penalize outliers by doubling the distance
140  distances[i] = 2 * std::sqrt (sqr_distance);
141  }
142  }
143 }
144 
145 //////////////////////////////////////////////////////////////////////////
146 template <typename PointT> void
148  const Eigen::VectorXf &model_coefficients, const double threshold, Indices &inliers)
149 {
150  // Needs a valid set of model coefficients
151  if (!isModelValid (model_coefficients))
152  {
153  PCL_ERROR ("[pcl::SampleConsensusModelStick::selectWithinDistance] Given model is invalid!\n");
154  return;
155  }
156 
157  float sqr_threshold = static_cast<float> (threshold * threshold);
158 
159  inliers.clear ();
160  error_sqr_dists_.clear ();
161  inliers.reserve (indices_->size ());
162  error_sqr_dists_.reserve (indices_->size ());
163 
164  // Obtain the line point and direction
165  Eigen::Vector4f line_pt1 (model_coefficients[0], model_coefficients[1], model_coefficients[2], 0.0f);
166  Eigen::Vector4f line_pt2 (model_coefficients[3], model_coefficients[4], model_coefficients[5], 0.0f);
167  Eigen::Vector4f line_dir = line_pt2 - line_pt1;
168  //Eigen::Vector4f line_dir (model_coefficients[3], model_coefficients[4], model_coefficients[5], 0);
169  //Eigen::Vector4f line_dir (model_coefficients[3] - model_coefficients[0], model_coefficients[4] - model_coefficients[1], model_coefficients[5] - model_coefficients[2], 0);
170  line_dir.normalize ();
171  //float norm = line_dir.squaredNorm ();
172 
173  // Iterate through the 3d points and calculate the distances from them to the line
174  for (std::size_t i = 0; i < indices_->size (); ++i)
175  {
176  // Calculate the distance from the point to the line
177  // D = ||(P2-P1) x (P1-P0)|| / ||P2-P1|| = norm (cross (p2-p1, p2-p0)) / norm(p2-p1)
178  Eigen::Vector4f dir = (*input_)[(*indices_)[i]].getVector4fMap () - line_pt1;
179  //float u = dir.dot (line_dir);
180 
181  // If the point falls outside of the segment, ignore it
182  //if (u < 0.0f || u > 1.0f)
183  // continue;
184 
185  float sqr_distance = dir.cross3 (line_dir).squaredNorm ();
186  if (sqr_distance < sqr_threshold)
187  {
188  // Returns the indices of the points whose squared distances are smaller than the threshold
189  inliers.push_back ((*indices_)[i]);
190  error_sqr_dists_.push_back (static_cast<double> (sqr_distance));
191  }
192  }
193 }
194 
195 ///////////////////////////////////////////////////////////////////////////
196 template <typename PointT> std::size_t
198  const Eigen::VectorXf &model_coefficients, const double threshold) const
199 {
200  // Needs a valid set of model coefficients
201  if (!isModelValid (model_coefficients))
202  {
203  PCL_ERROR ("[pcl::SampleConsensusModelStick::countWithinDistance] Given model is invalid!\n");
204  return (0);
205  }
206 
207  float sqr_threshold = static_cast<float> (threshold * threshold);
208 
209  std::size_t nr_i = 0, nr_o = 0;
210 
211  // Obtain the line point and direction
212  Eigen::Vector4f line_pt1 (model_coefficients[0], model_coefficients[1], model_coefficients[2], 0.0f);
213  Eigen::Vector4f line_pt2 (model_coefficients[3], model_coefficients[4], model_coefficients[5], 0.0f);
214  Eigen::Vector4f line_dir = line_pt2 - line_pt1;
215  line_dir.normalize ();
216 
217  //Eigen::Vector4f line_dir (model_coefficients[3] - model_coefficients[0], model_coefficients[4] - model_coefficients[1], model_coefficients[5] - model_coefficients[2], 0);
218  //Eigen::Vector4f line_dir (model_coefficients[3], model_coefficients[4], model_coefficients[5], 0);
219 
220  // Iterate through the 3d points and calculate the distances from them to the line
221  for (std::size_t i = 0; i < indices_->size (); ++i)
222  {
223  // Calculate the distance from the point to the line
224  // D = ||(P2-P1) x (P1-P0)|| / ||P2-P1|| = norm (cross (p2-p1, p2-p0)) / norm(p2-p1)
225  Eigen::Vector4f dir = (*input_)[(*indices_)[i]].getVector4fMap () - line_pt1;
226  //float u = dir.dot (line_dir);
227 
228  // If the point falls outside of the segment, ignore it
229  //if (u < 0.0f || u > 1.0f)
230  // continue;
231 
232  float sqr_distance = dir.cross3 (line_dir).squaredNorm ();
233  // Use a larger threshold (4 times the radius) to get more points in
234  if (sqr_distance < sqr_threshold)
235  {
236  nr_i++;
237  }
238  else if (sqr_distance < 4.0f * sqr_threshold)
239  {
240  nr_o++;
241  }
242  }
243 
244  return (nr_i <= nr_o ? 0 : nr_i - nr_o);
245 }
246 
247 //////////////////////////////////////////////////////////////////////////
248 template <typename PointT> void
250  const Indices &inliers, const Eigen::VectorXf &model_coefficients, Eigen::VectorXf &optimized_coefficients) const
251 {
252  // Needs a valid set of model coefficients
253  if (!isModelValid (model_coefficients))
254  {
255  optimized_coefficients = model_coefficients;
256  return;
257  }
258 
259  // Need more than the minimum sample size to make a difference
260  if (inliers.size () <= sample_size_)
261  {
262  PCL_ERROR ("[pcl::SampleConsensusModelStick::optimizeModelCoefficients] Not enough inliers to refine/optimize the model's coefficients (%lu)! Returning the same coefficients.\n", inliers.size ());
263  optimized_coefficients = model_coefficients;
264  return;
265  }
266 
267  optimized_coefficients.resize (model_size_);
268 
269  // Compute the 3x3 covariance matrix
270  Eigen::Vector4f centroid;
271  Eigen::Matrix3f covariance_matrix;
272 
273  if (0 == computeMeanAndCovarianceMatrix (*input_, inliers, covariance_matrix, centroid))
274  {
275  PCL_ERROR ("[pcl::SampleConsensusModelStick::optimizeModelCoefficients] computeMeanAndCovarianceMatrix failed (returned 0) because there are no valid inliers.\n");
276  optimized_coefficients = model_coefficients;
277  return;
278  }
279 
280  optimized_coefficients[0] = centroid[0];
281  optimized_coefficients[1] = centroid[1];
282  optimized_coefficients[2] = centroid[2];
283 
284  // Extract the eigenvalues and eigenvectors
285  Eigen::Vector3f eigen_values;
286  Eigen::Vector3f eigen_vector;
287  pcl::eigen33 (covariance_matrix, eigen_values);
288  pcl::computeCorrespondingEigenVector (covariance_matrix, eigen_values [2], eigen_vector);
289 
290  optimized_coefficients.template segment<3> (3).matrix () = eigen_vector;
291 }
292 
293 //////////////////////////////////////////////////////////////////////////
294 template <typename PointT> void
296  const Indices &inliers, const Eigen::VectorXf &model_coefficients, PointCloud &projected_points, bool copy_data_fields) const
297 {
298  // Needs a valid model coefficients
299  if (!isModelValid (model_coefficients))
300  {
301  PCL_ERROR ("[pcl::SampleConsensusModelStick::projectPoints] Given model is invalid!\n");
302  return;
303  }
304 
305  // Obtain the line point and direction
306  Eigen::Vector4f line_pt (model_coefficients[0], model_coefficients[1], model_coefficients[2], 0.0f);
307  Eigen::Vector4f line_dir (model_coefficients[3], model_coefficients[4], model_coefficients[5], 0.0f);
308 
309  projected_points.header = input_->header;
310  projected_points.is_dense = input_->is_dense;
311 
312  // Copy all the data fields from the input cloud to the projected one?
313  if (copy_data_fields)
314  {
315  // Allocate enough space and copy the basics
316  projected_points.resize (input_->size ());
317  projected_points.width = input_->width;
318  projected_points.height = input_->height;
319 
320  using FieldList = typename pcl::traits::fieldList<PointT>::type;
321  // Iterate over each point
322  for (std::size_t i = 0; i < projected_points.size (); ++i)
323  {
324  // Iterate over each dimension
325  pcl::for_each_type <FieldList> (NdConcatenateFunctor <PointT, PointT> ((*input_)[i], projected_points[i]));
326  }
327 
328  // Iterate through the 3d points and calculate the distances from them to the line
329  for (const auto &inlier : inliers)
330  {
331  Eigen::Vector4f pt ((*input_)[inlier].x, (*input_)[inlier].y, (*input_)[inlier].z, 0.0f);
332  // double k = (DOT_PROD_3D (points[i], p21) - dotA_B) / dotB_B;
333  float k = (pt.dot (line_dir) - line_pt.dot (line_dir)) / line_dir.dot (line_dir);
334 
335  Eigen::Vector4f pp = line_pt + k * line_dir;
336  // Calculate the projection of the point on the line (pointProj = A + k * B)
337  projected_points[inlier].x = pp[0];
338  projected_points[inlier].y = pp[1];
339  projected_points[inlier].z = pp[2];
340  }
341  }
342  else
343  {
344  // Allocate enough space and copy the basics
345  projected_points.resize (inliers.size ());
346  projected_points.width = inliers.size ();
347  projected_points.height = 1;
348 
349  using FieldList = typename pcl::traits::fieldList<PointT>::type;
350  // Iterate over each point
351  for (std::size_t i = 0; i < inliers.size (); ++i)
352  {
353  // Iterate over each dimension
354  pcl::for_each_type <FieldList> (NdConcatenateFunctor <PointT, PointT> ((*input_)[inliers[i]], projected_points[i]));
355  }
356 
357  // Iterate through the 3d points and calculate the distances from them to the line
358  for (std::size_t i = 0; i < inliers.size (); ++i)
359  {
360  Eigen::Vector4f pt ((*input_)[inliers[i]].x, (*input_)[inliers[i]].y, (*input_)[inliers[i]].z, 0.0f);
361  // double k = (DOT_PROD_3D (points[i], p21) - dotA_B) / dotB_B;
362  float k = (pt.dot (line_dir) - line_pt.dot (line_dir)) / line_dir.dot (line_dir);
363 
364  Eigen::Vector4f pp = line_pt + k * line_dir;
365  // Calculate the projection of the point on the line (pointProj = A + k * B)
366  projected_points[i].x = pp[0];
367  projected_points[i].y = pp[1];
368  projected_points[i].z = pp[2];
369  }
370  }
371 }
372 
373 //////////////////////////////////////////////////////////////////////////
374 template <typename PointT> bool
376  const std::set<index_t> &indices, const Eigen::VectorXf &model_coefficients, const double threshold) const
377 {
378  // Needs a valid set of model coefficients
379  if (!isModelValid (model_coefficients))
380  {
381  PCL_ERROR ("[pcl::SampleConsensusModelStick::doSamplesVerifyModel] Given model is invalid!\n");
382  return (false);
383  }
384 
385  // Obtain the line point and direction
386  Eigen::Vector4f line_pt (model_coefficients[0], model_coefficients[1], model_coefficients[2], 0.0f);
387  Eigen::Vector4f line_dir (model_coefficients[3] - model_coefficients[0], model_coefficients[4] - model_coefficients[1], model_coefficients[5] - model_coefficients[2], 0.0f);
388  //Eigen::Vector4f line_dir (model_coefficients[3], model_coefficients[4], model_coefficients[5], 0);
389  line_dir.normalize ();
390 
391  float sqr_threshold = static_cast<float> (threshold * threshold);
392  // Iterate through the 3d points and calculate the distances from them to the line
393  for (const auto &index : indices)
394  {
395  // Calculate the distance from the point to the line
396  // D = ||(P2-P1) x (P1-P0)|| / ||P2-P1|| = norm (cross (p2-p1, p2-p0)) / norm(p2-p1)
397  if ((line_pt - (*input_)[index].getVector4fMap ()).cross3 (line_dir).squaredNorm () > sqr_threshold)
398  {
399  return (false);
400  }
401  }
402 
403  return (true);
404 }
405 
406 #define PCL_INSTANTIATE_SampleConsensusModelStick(T) template class PCL_EXPORTS pcl::SampleConsensusModelStick<T>;
407 
408 #endif // PCL_SAMPLE_CONSENSUS_IMPL_SAC_MODEL_STICK_H_
409 
Define methods for centroid estimation and covariance matrix calculus.
PointCloud represents the base class in PCL for storing collections of 3D points.
Definition: point_cloud.h:173
bool is_dense
True if no points are invalid (e.g., have NaN or Inf values in any of their floating point fields).
Definition: point_cloud.h:403
void resize(std::size_t count)
Resizes the container to contain count elements.
Definition: point_cloud.h:462
std::uint32_t width
The point cloud width (if organized as an image-structure).
Definition: point_cloud.h:398
pcl::PCLHeader header
The point cloud header.
Definition: point_cloud.h:392
std::uint32_t height
The point cloud height (if organized as an image-structure).
Definition: point_cloud.h:400
std::size_t size() const
Definition: point_cloud.h:443
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 stick model coefficients.
bool computeModelCoefficients(const Indices &samples, Eigen::VectorXf &model_coefficients) const override
Check whether the given index samples can form a valid stick model, compute the model coefficients fr...
void getDistancesToModel(const Eigen::VectorXf &model_coefficients, std::vector< double > &distances) const override
Compute all squared distances from the cloud data to a given stick model.
bool isSampleGood(const Indices &samples) const override
Check if a sample of indices results in a good sample of points indices.
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 stick model.
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 optimizeModelCoefficients(const Indices &inliers, const Eigen::VectorXf &model_coefficients, Eigen::VectorXf &optimized_coefficients) const override
Recompute the stick coefficients using the given inlier set and return them to the user.
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 computeMeanAndCovarianceMatrix(const pcl::PointCloud< PointT > &cloud, Eigen::Matrix< Scalar, 3, 3 > &covariance_matrix, Eigen::Matrix< Scalar, 4, 1 > &centroid)
Compute the normalized 3x3 covariance matrix and the centroid of a given set of points in a single lo...
Definition: centroid.hpp:508
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
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition: types.h:133
Helper functor structure for concatenate.
Definition: concatenate.h:50