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  return (true);
102 }
103 
104 //////////////////////////////////////////////////////////////////////////
105 template <typename PointT> void
107  const Eigen::VectorXf &model_coefficients, std::vector<double> &distances) const
108 {
109  // Needs a valid set of model coefficients
110  if (!isModelValid (model_coefficients))
111  {
112  return;
113  }
114 
115  distances.resize (indices_->size ());
116 
117  // Obtain the line point and direction
118  Eigen::Vector4f line_pt (model_coefficients[0], model_coefficients[1], model_coefficients[2], 0);
119  Eigen::Vector4f line_dir (model_coefficients[3], model_coefficients[4], model_coefficients[5], 0);
120  line_dir.normalize ();
121 
122  // Iterate through the 3d points and calculate the distances from them to the line
123  for (std::size_t i = 0; i < indices_->size (); ++i)
124  {
125  // Calculate the distance from the point to the line
126  // D = ||(P2-P1) x (P1-P0)|| / ||P2-P1|| = norm (cross (p2-p1, p2-p0)) / norm(p2-p1)
127  // Need to estimate sqrt here to keep MSAC and friends general
128  distances[i] = sqrt ((line_pt - (*input_)[(*indices_)[i]].getVector4fMap ()).cross3 (line_dir).squaredNorm ());
129  }
130 }
131 
132 //////////////////////////////////////////////////////////////////////////
133 template <typename PointT> void
135  const Eigen::VectorXf &model_coefficients, const double threshold, Indices &inliers)
136 {
137  // Needs a valid set of model coefficients
138  if (!isModelValid (model_coefficients))
139  return;
140 
141  double sqr_threshold = threshold * threshold;
142 
143  inliers.clear ();
144  error_sqr_dists_.clear ();
145  inliers.reserve (indices_->size ());
146  error_sqr_dists_.reserve (indices_->size ());
147 
148  // Obtain the line point and direction
149  Eigen::Vector4f line_pt (model_coefficients[0], model_coefficients[1], model_coefficients[2], 0);
150  Eigen::Vector4f line_dir (model_coefficients[3], model_coefficients[4], model_coefficients[5], 0);
151  line_dir.normalize ();
152 
153  // Iterate through the 3d points and calculate the distances from them to the line
154  for (std::size_t i = 0; i < indices_->size (); ++i)
155  {
156  // Calculate the distance from the point to the line
157  // D = ||(P2-P1) x (P1-P0)|| / ||P2-P1|| = norm (cross (p2-p1, p2-p0)) / norm(p2-p1)
158  double sqr_distance = (line_pt - (*input_)[(*indices_)[i]].getVector4fMap ()).cross3 (line_dir).squaredNorm ();
159 
160  if (sqr_distance < sqr_threshold)
161  {
162  // Returns the indices of the points whose squared distances are smaller than the threshold
163  inliers.push_back ((*indices_)[i]);
164  error_sqr_dists_.push_back (sqr_distance);
165  }
166  }
167 }
168 
169 //////////////////////////////////////////////////////////////////////////
170 template <typename PointT> std::size_t
172  const Eigen::VectorXf &model_coefficients, const double threshold) const
173 {
174  // Needs a valid set of model coefficients
175  if (!isModelValid (model_coefficients))
176  return (0);
177 
178  double sqr_threshold = threshold * threshold;
179 
180  std::size_t nr_p = 0;
181 
182  // Obtain the line point and direction
183  Eigen::Vector4f line_pt (model_coefficients[0], model_coefficients[1], model_coefficients[2], 0.0f);
184  Eigen::Vector4f line_dir (model_coefficients[3], model_coefficients[4], model_coefficients[5], 0.0f);
185  line_dir.normalize ();
186 
187  // Iterate through the 3d points and calculate the distances from them to the line
188  for (std::size_t i = 0; i < indices_->size (); ++i)
189  {
190  // Calculate the distance from the point to the line
191  // D = ||(P2-P1) x (P1-P0)|| / ||P2-P1|| = norm (cross (p2-p1, p2-p0)) / norm(p2-p1)
192  double sqr_distance = (line_pt - (*input_)[(*indices_)[i]].getVector4fMap ()).cross3 (line_dir).squaredNorm ();
193 
194  if (sqr_distance < sqr_threshold)
195  nr_p++;
196  }
197  return (nr_p);
198 }
199 
200 //////////////////////////////////////////////////////////////////////////
201 template <typename PointT> void
203  const Indices &inliers, const Eigen::VectorXf &model_coefficients, Eigen::VectorXf &optimized_coefficients) const
204 {
205  // Needs a valid set of model coefficients
206  if (!isModelValid (model_coefficients))
207  {
208  optimized_coefficients = model_coefficients;
209  return;
210  }
211 
212  // Need more than the minimum sample size to make a difference
213  if (inliers.size () <= sample_size_)
214  {
215  PCL_ERROR ("[pcl::SampleConsensusModelLine::optimizeModelCoefficients] Not enough inliers to refine/optimize the model's coefficients (%lu)! Returning the same coefficients.\n", inliers.size ());
216  optimized_coefficients = model_coefficients;
217  return;
218  }
219 
220  optimized_coefficients.resize (model_size_);
221 
222  // Compute the 3x3 covariance matrix
223  Eigen::Vector4f centroid;
224  if (0 == compute3DCentroid (*input_, inliers, centroid))
225  {
226  PCL_WARN ("[pcl::SampleConsensusModelLine::optimizeModelCoefficients] compute3DCentroid failed (returned 0) because there are no valid inliers.\n");
227  optimized_coefficients = model_coefficients;
228  return;
229  }
230  Eigen::Matrix3f covariance_matrix;
231  computeCovarianceMatrix (*input_, inliers, centroid, covariance_matrix);
232  optimized_coefficients[0] = centroid[0];
233  optimized_coefficients[1] = centroid[1];
234  optimized_coefficients[2] = centroid[2];
235 
236  // Extract the eigenvalues and eigenvectors
237  EIGEN_ALIGN16 Eigen::Vector3f eigen_values;
238  EIGEN_ALIGN16 Eigen::Vector3f eigen_vector;
239  pcl::eigen33 (covariance_matrix, eigen_values);
240  pcl::computeCorrespondingEigenVector (covariance_matrix, eigen_values [2], eigen_vector);
241  //pcl::eigen33 (covariance_matrix, eigen_vectors, eigen_values);
242 
243  optimized_coefficients.template tail<3> ().matrix () = eigen_vector;
244 }
245 
246 //////////////////////////////////////////////////////////////////////////
247 template <typename PointT> void
249  const Indices &inliers, const Eigen::VectorXf &model_coefficients, PointCloud &projected_points, bool copy_data_fields) const
250 {
251  // Needs a valid model coefficients
252  if (!isModelValid (model_coefficients))
253  return;
254 
255  // Obtain the line point and direction
256  Eigen::Vector4f line_pt (model_coefficients[0], model_coefficients[1], model_coefficients[2], 0.0f);
257  Eigen::Vector4f line_dir (model_coefficients[3], model_coefficients[4], model_coefficients[5], 0.0f);
258 
259  projected_points.header = input_->header;
260  projected_points.is_dense = input_->is_dense;
261 
262  // Copy all the data fields from the input cloud to the projected one?
263  if (copy_data_fields)
264  {
265  // Allocate enough space and copy the basics
266  projected_points.resize (input_->size ());
267  projected_points.width = input_->width;
268  projected_points.height = input_->height;
269 
270  using FieldList = typename pcl::traits::fieldList<PointT>::type;
271  // Iterate over each point
272  for (std::size_t i = 0; i < projected_points.size (); ++i)
273  // Iterate over each dimension
274  pcl::for_each_type <FieldList> (NdConcatenateFunctor <PointT, PointT> ((*input_)[i], projected_points[i]));
275 
276  // Iterate through the 3d points and calculate the distances from them to the line
277  for (const auto &inlier : inliers)
278  {
279  Eigen::Vector4f pt ((*input_)[inlier].x, (*input_)[inlier].y, (*input_)[inlier].z, 0.0f);
280  // double k = (DOT_PROD_3D (points[i], p21) - dotA_B) / dotB_B;
281  float k = (pt.dot (line_dir) - line_pt.dot (line_dir)) / line_dir.dot (line_dir);
282 
283  Eigen::Vector4f pp = line_pt + k * line_dir;
284  // Calculate the projection of the point on the line (pointProj = A + k * B)
285  projected_points[inlier].x = pp[0];
286  projected_points[inlier].y = pp[1];
287  projected_points[inlier].z = pp[2];
288  }
289  }
290  else
291  {
292  // Allocate enough space and copy the basics
293  projected_points.resize (inliers.size ());
294  projected_points.width = inliers.size ();
295  projected_points.height = 1;
296 
297  using FieldList = typename pcl::traits::fieldList<PointT>::type;
298  // Iterate over each point
299  for (std::size_t i = 0; i < inliers.size (); ++i)
300  // Iterate over each dimension
301  pcl::for_each_type <FieldList> (NdConcatenateFunctor <PointT, PointT> ((*input_)[inliers[i]], projected_points[i]));
302 
303  // Iterate through the 3d points and calculate the distances from them to the line
304  for (std::size_t i = 0; i < inliers.size (); ++i)
305  {
306  Eigen::Vector4f pt ((*input_)[inliers[i]].x, (*input_)[inliers[i]].y, (*input_)[inliers[i]].z, 0.0f);
307  // double k = (DOT_PROD_3D (points[i], p21) - dotA_B) / dotB_B;
308  float k = (pt.dot (line_dir) - line_pt.dot (line_dir)) / line_dir.dot (line_dir);
309 
310  Eigen::Vector4f pp = line_pt + k * line_dir;
311  // Calculate the projection of the point on the line (pointProj = A + k * B)
312  projected_points[i].x = pp[0];
313  projected_points[i].y = pp[1];
314  projected_points[i].z = pp[2];
315  }
316  }
317 }
318 
319 //////////////////////////////////////////////////////////////////////////
320 template <typename PointT> bool
322  const std::set<index_t> &indices, const Eigen::VectorXf &model_coefficients, const double threshold) const
323 {
324  // Needs a valid set of model coefficients
325  if (!isModelValid (model_coefficients))
326  return (false);
327 
328  // Obtain the line point and direction
329  Eigen::Vector4f line_pt (model_coefficients[0], model_coefficients[1], model_coefficients[2], 0.0f);
330  Eigen::Vector4f line_dir (model_coefficients[3], model_coefficients[4], model_coefficients[5], 0.0f);
331  line_dir.normalize ();
332 
333  double sqr_threshold = threshold * threshold;
334  // Iterate through the 3d points and calculate the distances from them to the line
335  for (const auto &index : indices)
336  {
337  // Calculate the distance from the point to the line
338  // D = ||(P2-P1) x (P1-P0)|| / ||P2-P1|| = norm (cross (p2-p1, p2-p0)) / norm(p2-p1)
339  if ((line_pt - (*input_)[index].getVector4fMap ()).cross3 (line_dir).squaredNorm () > sqr_threshold)
340  return (false);
341  }
342 
343  return (true);
344 }
345 
346 #define PCL_INSTANTIATE_SampleConsensusModelLine(T) template class PCL_EXPORTS pcl::SampleConsensusModelLine<T>;
347 
348 #endif // PCL_SAMPLE_CONSENSUS_IMPL_SAC_MODEL_LINE_H_
349 
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:248
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:134
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:321
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:131
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:202
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:106
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:171
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