correspondence_rejection_sample_consensus_2d.hpp

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#ifndef PCL_REGISTRATION_IMPL_CORRESPONDENCE_REJECTION_SAMPLE_CONSENSUS_2D_HPP_
#define PCL_REGISTRATION_IMPL_CORRESPONDENCE_REJECTION_SAMPLE_CONSENSUS_2D_HPP_

#include <pcl/sample_consensus/sac_model_registration_2d.h>
#include <pcl/sample_consensus/ransac.h>

template <typename PointT> void 
pcl::registration::CorrespondenceRejectorSampleConsensus2D<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 (projection_matrix_ == Eigen::Matrix3f::Identity ())
  {
    PCL_ERROR ("[pcl::registration::%s::getRemainingCorrespondences] Intrinsic camera parameters not given!\n", getClassName ().c_str ());
    return;
  }

  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 (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 pcl/registration/icp.hpp:
  std::vector<int> source_indices_good;
  std::vector<int> target_indices_good;

  // From the set of correspondences found, attempt to remove outliers
  typename pcl::SampleConsensusModelRegistration2D<PointT>::Ptr model (new pcl::SampleConsensusModelRegistration2D<PointT> (input_, source_indices));
  // Pass the target_indices
  model->setInputTarget (target_, target_indices);
  model->setProjectionMatrix (projection_matrix_);

  // Create a RANSAC model
  pcl::RandomSampleConsensus<PointT> sac (model, inlier_threshold_);
  sac.setMaxIterations (max_iterations_);

  // Compute the set of inliers
  if (!sac.computeModel ())
  {
    PCL_ERROR ("[pcl::registration::%s::getRemainingCorrespondences] Error computing model! Returning the original correspondences...\n", getClassName ().c_str ());
    remaining_correspondences = original_correspondences;
    best_transformation_.setIdentity ();
    return;
  }
  else
  {
    if (refine_ && !sac.refineModel (2.0))
      PCL_WARN ("[pcl::registration::%s::getRemainingCorrespondences] Error refining model!\n", getClassName ().c_str ());
      
    std::vector<int> inliers;
    sac.getInliers (inliers);

    if (inliers.size () < 3)
    {
      PCL_ERROR ("[pcl::registration::%s::getRemainingCorrespondences] Less than 3 correspondences found!\n", getClassName ().c_str ());
      remaining_correspondences = original_correspondences;
      best_transformation_.setIdentity ();
      return;
    }

    boost::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 (size_t i = 0; i < inliers.size (); ++i)
      remaining_correspondences[i] = original_correspondences[index_to_correspondence[inliers[i]]];

    // 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);
  }
}

#endif    // PCL_REGISTRATION_IMPL_CORRESPONDENCE_REJECTION_SAMPLE_CONSENSUS_2D_HPP_