covariance_sampling.hpp

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#ifndef PCL_FILTERS_IMPL_COVARIANCE_SAMPLING_H_
#define PCL_FILTERS_IMPL_COVARIANCE_SAMPLING_H_
 
#include <pcl/filters/covariance_sampling.h>
#include <list>
#include <Eigen/Eigenvalues> // for SelfAdjointEigenSolver
 
///////////////////////////////////////////////////////////////////////////////
template<typename PointT, typename PointNT> bool
pcl::CovarianceSampling<PointT, PointNT>::initCompute ()
{
  if (!FilterIndices<PointT>::initCompute ())
    return false;
 
  if (num_samples_ > indices_->size ())
  {
    PCL_ERROR ("[pcl::CovarianceSampling::initCompute] The number of samples you asked for (%d) is larger than the number of input indices (%lu)\n",
               num_samples_, indices_->size ());
    return false;
  }
 
  // Prepare the point cloud by centering at the origin and then scaling the points such that the average distance from
  // the origin is 1.0 => rotations and translations will have the same magnitude
  Eigen::Vector3f centroid (0.f, 0.f, 0.f);
  for (std::size_t p_i = 0; p_i < indices_->size (); ++p_i)
    centroid += (*input_)[(*indices_)[p_i]].getVector3fMap ();
  centroid /= float (indices_->size ());
 
  scaled_points_.resize (indices_->size ());
  double average_norm = 0.0;
  for (std::size_t p_i = 0; p_i < indices_->size (); ++p_i)
  {
    scaled_points_[p_i] = (*input_)[(*indices_)[p_i]].getVector3fMap () - centroid;
    average_norm += scaled_points_[p_i].norm ();
  }
  average_norm /= double (scaled_points_.size ());
  for (std::size_t p_i = 0; p_i < scaled_points_.size (); ++p_i)
    scaled_points_[p_i] /= float (average_norm);
 
  return (true);
}
 
///////////////////////////////////////////////////////////////////////////////
template<typename PointT, typename PointNT> double
pcl::CovarianceSampling<PointT, PointNT>::computeConditionNumber ()
{
  Eigen::Matrix<double, 6, 6> covariance_matrix;
  if (!computeCovarianceMatrix (covariance_matrix))
    return (-1.);
 
  return computeConditionNumber (covariance_matrix);
}
 
 
///////////////////////////////////////////////////////////////////////////////
template<typename PointT, typename PointNT> double
pcl::CovarianceSampling<PointT, PointNT>::computeConditionNumber (const Eigen::Matrix<double, 6, 6> &covariance_matrix)
{
  const Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, 6, 6> > solver (covariance_matrix, Eigen::EigenvaluesOnly);
  const double max_ev = solver.eigenvalues (). maxCoeff ();
  const double min_ev = solver.eigenvalues (). minCoeff ();
  return (max_ev / min_ev);
}
 
 
///////////////////////////////////////////////////////////////////////////////
template<typename PointT, typename PointNT> bool
pcl::CovarianceSampling<PointT, PointNT>::computeCovarianceMatrix (Eigen::Matrix<double, 6, 6> &covariance_matrix)
{
  if (!initCompute ())
    return false;
 
  //--- Part A from the paper
  // Set up matrix F
  Eigen::Matrix<double, 6, Eigen::Dynamic> f_mat = Eigen::Matrix<double, 6, Eigen::Dynamic> (6, indices_->size ());
  for (std::size_t p_i = 0; p_i < scaled_points_.size (); ++p_i)
  {
    f_mat.block<3, 1> (0, p_i) = scaled_points_[p_i].cross (
                                     (*input_normals_)[(*indices_)[p_i]].getNormalVector3fMap ()).template cast<double> ();
    f_mat.block<3, 1> (3, p_i) = (*input_normals_)[(*indices_)[p_i]].getNormalVector3fMap ().template cast<double> ();
  }
 
  // Compute the covariance matrix C and its 6 eigenvectors (initially complex, move them to a double matrix)
  covariance_matrix = f_mat * f_mat.transpose ();
  return true;
}
 
///////////////////////////////////////////////////////////////////////////////
template<typename PointT, typename PointNT> void
pcl::CovarianceSampling<PointT, PointNT>::applyFilter (Indices &sampled_indices)
{
  Eigen::Matrix<double, 6, 6> c_mat;
  // Invokes initCompute()
  if (!computeCovarianceMatrix (c_mat))
    return;
 
  const Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, 6, 6> > solver (c_mat);
  const Eigen::Matrix<double, 6, 6> x = solver.eigenvectors ();
 
  //--- Part B from the paper
  /// TODO figure out how to fill the candidate_indices - see subsequent paper paragraphs
  std::vector<std::size_t> candidate_indices;
  candidate_indices.resize (indices_->size ());
  for (std::size_t p_i = 0; p_i < candidate_indices.size (); ++p_i)
    candidate_indices[p_i] = p_i;
 
  // Compute the v 6-vectors
  using Vector6d = Eigen::Matrix<double, 6, 1>;
  std::vector<Vector6d, Eigen::aligned_allocator<Vector6d> > v;
  v.resize (candidate_indices.size ());
  for (std::size_t p_i = 0; p_i < candidate_indices.size (); ++p_i)
  {
    v[p_i].block<3, 1> (0, 0) = scaled_points_[p_i].cross (
                                  (*input_normals_)[(*indices_)[candidate_indices[p_i]]].getNormalVector3fMap ()).template cast<double> ();
    v[p_i].block<3, 1> (3, 0) = (*input_normals_)[(*indices_)[candidate_indices[p_i]]].getNormalVector3fMap ().template cast<double> ();
  }
 
 
  // Set up the lists to be sorted
  std::vector<std::list<std::pair<int, double> > > L;
  L.resize (6);
 
  for (std::size_t i = 0; i < 6; ++i)
  {
    for (std::size_t p_i = 0; p_i < candidate_indices.size (); ++p_i)
      L[i].push_back (std::make_pair (p_i, std::abs (v[p_i].dot (x.block<6, 1> (0, i)))));
 
    // Sort in decreasing order
    L[i].sort (sort_dot_list_function);
  }
 
  // Initialize the 6 t's
  std::vector<double> t (6, 0.0);
 
  sampled_indices.resize (num_samples_);
  std::vector<bool> point_sampled (candidate_indices.size (), false);
  // Now select the actual points
  for (std::size_t sample_i = 0; sample_i < num_samples_; ++sample_i)
  {
    // Find the most unconstrained dimension, i.e., the minimum t
    std::size_t min_t_i = 0;
    for (std::size_t i = 0; i < 6; ++i)
    {
      if (t[min_t_i] > t[i])
        min_t_i = i;
    }
 
    // Add the point from the top of the list corresponding to the dimension to the set of samples
    while (point_sampled [L[min_t_i].front ().first])
      L[min_t_i].pop_front ();
 
    sampled_indices[sample_i] = L[min_t_i].front ().first;
    point_sampled[L[min_t_i].front ().first] = true;
    L[min_t_i].pop_front ();
 
    // Update the running totals
    for (std::size_t i = 0; i < 6; ++i)
    {
      double val = v[sampled_indices[sample_i]].dot (x.block<6, 1> (0, i));
      t[i] += val * val;
    }
  }
 
  // Remap the sampled_indices to the input_ cloud
  for (auto &sampled_index : sampled_indices)
    sampled_index = (*indices_)[candidate_indices[sampled_index]];
}
 
 
///////////////////////////////////////////////////////////////////////////////
template<typename PointT, typename PointNT> void
pcl::CovarianceSampling<PointT, PointNT>::applyFilter (Cloud &output)
{
  Indices sampled_indices;
  applyFilter (sampled_indices);
 
  output.resize (sampled_indices.size ());
  output.header = input_->header;
  output.height = 1;
  output.width = output.size ();
  output.is_dense = true;
  for (std::size_t i = 0; i < sampled_indices.size (); ++i)
    output[i] = (*input_)[sampled_indices[i]];
}
 
 
#define PCL_INSTANTIATE_CovarianceSampling(T,NT) template class PCL_EXPORTS pcl::CovarianceSampling<T,NT>;
 
#endif /* PCL_FILTERS_IMPL_COVARIANCE_SAMPLING_H_ */