transformation_validation_euclidean.hpp

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#ifndef PCL_REGISTRATION_TRANSFORMATION_VALIDATION_EUCLIDEAN_IMPL_H_
#define PCL_REGISTRATION_TRANSFORMATION_VALIDATION_EUCLIDEAN_IMPL_H_
 
#include <pcl/search/auto.h>
 
namespace pcl {
 
namespace registration {
 
template <typename PointSource, typename PointTarget, typename Scalar>
double
TransformationValidationEuclidean<PointSource, PointTarget, Scalar>::
    validateTransformation(const PointCloudSourceConstPtr& cloud_src,
                           const PointCloudTargetConstPtr& cloud_tgt,
                           const Matrix4& transformation_matrix) const
{
  double fitness_score = 0.0;
 
  // Transform the input dataset using the final transformation
  pcl::PointCloud<PointSource> input_transformed;
  // transformPointCloud (*cloud_src, input_transformed, transformation_matrix);
  input_transformed.resize(cloud_src->size());
  for (std::size_t i = 0; i < cloud_src->size(); ++i) {
    const PointSource& src = (*cloud_src)[i];
    PointTarget& tgt = input_transformed[i];
    tgt.x = static_cast<float>(
        transformation_matrix(0, 0) * src.x + transformation_matrix(0, 1) * src.y +
        transformation_matrix(0, 2) * src.z + transformation_matrix(0, 3));
    tgt.y = static_cast<float>(
        transformation_matrix(1, 0) * src.x + transformation_matrix(1, 1) * src.y +
        transformation_matrix(1, 2) * src.z + transformation_matrix(1, 3));
    tgt.z = static_cast<float>(
        transformation_matrix(2, 0) * src.x + transformation_matrix(2, 1) * src.y +
        transformation_matrix(2, 2) * src.z + transformation_matrix(2, 3));
  }
 
  typename MyPointRepresentation::ConstPtr point_rep(new MyPointRepresentation);
  KdTreePtr tree(
      pcl::search::autoSelectMethod<PointTarget>(cloud_tgt,
                                                 pcl::IndicesConstPtr(),
                                                 point_rep,
                                                 false,
                                                 pcl::search::Purpose::one_knn_search));
 
  pcl::Indices nn_indices(1);
  std::vector<float> nn_dists(1);
 
  // For each point in the source dataset
  int nr = 0;
  for (const auto& point : input_transformed) {
    // Find its nearest neighbor in the target
    tree->nearestKSearch(point, 1, nn_indices, nn_dists);
 
    // Deal with occlusions (incomplete targets)
    if (nn_dists[0] > max_range_)
      continue;
 
    // Calculate the fitness score
    fitness_score += nn_dists[0];
    ++nr;
  }
 
  if (nr > 0)
    return (fitness_score / nr);
  return (std::numeric_limits<double>::max());
}
 
} // namespace registration
} // namespace pcl
 
#endif // PCL_REGISTRATION_TRANSFORMATION_VALIDATION_EUCLIDEAN_IMPL_H_