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 <pcl/sample_consensus/ransac.h>
#include <pcl/sample_consensus/sac_model_registration.h>
 
#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());
  pcl::Indices source_indices(nr_correspondences);
  pcl::Indices 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;
    }
 
    pcl::Indices 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 auto& 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_