correspondence_rejection_sample_consensus.hpp

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#ifndef PCL_REGISTRATION_IMPL_CORRESPONDENCE_REJECTION_SAMPLE_CONSENSUS_HPP_
#define PCL_REGISTRATION_IMPL_CORRESPONDENCE_REJECTION_SAMPLE_CONSENSUS_HPP_
 
#include <unordered_map>
 
 
namespace pcl
{
 
namespace registration
{
 
template <typename PointT> void
CorrespondenceRejectorSampleConsensus<PointT>::getRemainingCorrespondences (
    const pcl::Correspondences& original_correspondences,
    pcl::Correspondences& remaining_correspondences)
{
  if (!input_)
  {
    PCL_ERROR ("[pcl::registration::%s::getRemainingCorrespondences] No input cloud dataset was given!\n", getClassName ().c_str ());
    return;
  }
 
  if (!target_)
  {
    PCL_ERROR ("[pcl::registration::%s::getRemainingCorrespondences] No input target dataset was given!\n", getClassName ().c_str ());
    return;
  }
 
  if (save_inliers_)
     inlier_indices_.clear ();
 
  int nr_correspondences = static_cast<int> (original_correspondences.size ());
  std::vector<int> source_indices (nr_correspondences);
  std::vector<int> target_indices (nr_correspondences);
 
  // Copy the query-match indices
  for (std::size_t i = 0; i < original_correspondences.size (); ++i)
  {
    source_indices[i] = original_correspondences[i].index_query;
    target_indices[i] = original_correspondences[i].index_match;
  }
 
   {
     // From the set of correspondences found, attempt to remove outliers
     // Create the registration model
     using SampleConsensusModelRegistrationPtr = typename pcl::SampleConsensusModelRegistration<PointT>::Ptr;
     SampleConsensusModelRegistrationPtr model;
     model.reset (new pcl::SampleConsensusModelRegistration<PointT> (input_, source_indices));
     // Pass the target_indices
     model->setInputTarget (target_, target_indices);
     // Create a RANSAC model
     pcl::RandomSampleConsensus<PointT> sac (model, inlier_threshold_);
     sac.setMaxIterations (max_iterations_);
 
     // Compute the set of inliers
     if (!sac.computeModel ())
     {
       remaining_correspondences = original_correspondences;
       best_transformation_.setIdentity ();
       return;
     }
     if (refine_ && !sac.refineModel ())
     {
       PCL_ERROR ("[pcl::registration::CorrespondenceRejectorSampleConsensus::getRemainingCorrespondences] Could not refine the model! Returning an empty solution.\n");
       return;
     }
 
     std::vector<int> inliers;
     sac.getInliers (inliers);
 
     if (inliers.size () < 3)
     {
       remaining_correspondences = original_correspondences;
       best_transformation_.setIdentity ();
       return;
     }
     std::unordered_map<int, int> index_to_correspondence;
     for (int i = 0; i < nr_correspondences; ++i)
       index_to_correspondence[original_correspondences[i].index_query] = i;
 
     remaining_correspondences.resize (inliers.size ());
     for (std::size_t i = 0; i < inliers.size (); ++i)
       remaining_correspondences[i] = original_correspondences[index_to_correspondence[inliers[i]]];
 
     if (save_inliers_)
     {
       inlier_indices_.reserve (inliers.size ());
       for (const int &inlier : inliers)
         inlier_indices_.push_back (index_to_correspondence[inlier]);
     }
 
     // get best transformation
     Eigen::VectorXf model_coefficients;
     sac.getModelCoefficients (model_coefficients);
     best_transformation_.row (0) = model_coefficients.segment<4>(0);
     best_transformation_.row (1) = model_coefficients.segment<4>(4);
     best_transformation_.row (2) = model_coefficients.segment<4>(8);
     best_transformation_.row (3) = model_coefficients.segment<4>(12);
   }
}
 
} // namespace registration
} // namespace pcl
 
#endif    // PCL_REGISTRATION_IMPL_CORRESPONDENCE_REJECTION_SAMPLE_CONSENSUS_HPP_