Point Cloud Library (PCL)  1.11.1-dev
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 //////////////////////////////////////////////////////////////////////////
50 template <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 //////////////////////////////////////////////////////////////////////////
73 template <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 //////////////////////////////////////////////////////////////////////////
108 template <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 //////////////////////////////////////////////////////////////////////////
136 template <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 //////////////////////////////////////////////////////////////////////////
173 template <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 //////////////////////////////////////////////////////////////////////////
204 template <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 //////////////////////////////////////////////////////////////////////////
250 template <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 //////////////////////////////////////////////////////////////////////////
323 template <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 
pcl::PointCloud::height
std::uint32_t height
The point cloud height (if organized as an image-structure).
Definition: point_cloud.h:394
pcl::SampleConsensusModelLine::projectPoints
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.
Definition: sac_model_line.hpp:251
pcl::NdConcatenateFunctor
Helper functor structure for concatenate.
Definition: concatenate.h:49
pcl::PointCloud
PointCloud represents the base class in PCL for storing collections of 3D points.
Definition: distances.h:55
pcl::SampleConsensusModelLine::isSampleGood
bool isSampleGood(const Indices &samples) const override
Check if a sample of indices results in a good sample of points indices.
Definition: sac_model_line.hpp:51
pcl::eigen33
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
pcl::SampleConsensusModelLine::selectWithinDistance
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.
Definition: sac_model_line.hpp:137
pcl::PointCloud::width
std::uint32_t width
The point cloud width (if organized as an image-structure).
Definition: point_cloud.h:392
pcl::SampleConsensusModelLine::doSamplesVerifyModel
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.
Definition: sac_model_line.hpp:324
pcl::computeCovarianceMatrix
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
pcl::PointCloud::is_dense
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:397
pcl::PointCloud::resize
void resize(std::size_t count)
Resizes the container to contain count elements.
Definition: point_cloud.h:456
pcl::PointCloud::header
pcl::PCLHeader header
The point cloud header.
Definition: point_cloud.h:386
pcl::Indices
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition: types.h:133
pcl::PointCloud::size
std::size_t size() const
Definition: point_cloud.h:437
pcl::SampleConsensusModelLine::optimizeModelCoefficients
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.
Definition: sac_model_line.hpp:205
pcl::SampleConsensusModelLine::getDistancesToModel
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.
Definition: sac_model_line.hpp:109
pcl::computeCorrespondingEigenVector
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
pcl::compute3DCentroid
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
pcl::SampleConsensusModelLine::countWithinDistance
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.
Definition: sac_model_line.hpp:174
pcl::SampleConsensusModelLine::computeModelCoefficients
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...
Definition: sac_model_line.hpp:74
centroid.h