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/ransac.h>
#include <pcl/sample_consensus/sac_model_registration_2d.h>
 
#include <unordered_map>
 
namespace pcl {
 
namespace registration {
 
template <typename PointT>
void
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 (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
  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;
  }
  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;
  }
 
  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]]];
 
  // 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_2D_HPP_