correspondence_estimation_normal_shooting.hpp

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#ifndef PCL_REGISTRATION_IMPL_CORRESPONDENCE_ESTIMATION_NORMAL_SHOOTING_H_
#define PCL_REGISTRATION_IMPL_CORRESPONDENCE_ESTIMATION_NORMAL_SHOOTING_H_
 
#include <pcl/common/copy_point.h>
 
namespace pcl {
 
namespace registration {
 
template <typename PointSource, typename PointTarget, typename NormalT, typename Scalar>
bool
CorrespondenceEstimationNormalShooting<PointSource, PointTarget, NormalT, Scalar>::
    initCompute()
{
  if (!source_normals_) {
    PCL_WARN("[pcl::registration::%s::initCompute] Datasets containing normals for "
             "source have not been given!\n",
             getClassName().c_str());
    return (false);
  }
 
  return (
      CorrespondenceEstimationBase<PointSource, PointTarget, Scalar>::initCompute());
}
 
template <typename PointSource, typename PointTarget, typename NormalT, typename Scalar>
void
CorrespondenceEstimationNormalShooting<PointSource, PointTarget, NormalT, Scalar>::
    determineCorrespondences(pcl::Correspondences& correspondences, double max_distance)
{
  if (!initCompute())
    return;
 
  correspondences.resize(indices_->size());
 
  std::vector<int> nn_indices(k_);
  std::vector<float> nn_dists(k_);
 
  int min_index = 0;
 
  pcl::Correspondence corr;
  unsigned int nr_valid_correspondences = 0;
 
  // Check if the template types are the same. If true, avoid a copy.
  // Both point types MUST be registered using the POINT_CLOUD_REGISTER_POINT_STRUCT
  // macro!
  if (isSamePointType<PointSource, PointTarget>()) {
    PointTarget pt;
    // Iterate over the input set of source indices
    for (const auto& idx_i : (*indices_)) {
      tree_->nearestKSearch((*input_)[idx_i], k_, nn_indices, nn_dists);
 
      // Among the K nearest neighbours find the one with minimum perpendicular distance
      // to the normal
      double min_dist = std::numeric_limits<double>::max();
 
      // Find the best correspondence
      for (std::size_t j = 0; j < nn_indices.size(); j++) {
        // computing the distance between a point and a line in 3d.
        // Reference - http://mathworld.wolfram.com/Point-LineDistance3-Dimensional.html
        pt.x = (*target_)[nn_indices[j]].x - (*input_)[idx_i].x;
        pt.y = (*target_)[nn_indices[j]].y - (*input_)[idx_i].y;
        pt.z = (*target_)[nn_indices[j]].z - (*input_)[idx_i].z;
 
        const NormalT& normal = (*source_normals_)[idx_i];
        Eigen::Vector3d N(normal.normal_x, normal.normal_y, normal.normal_z);
        Eigen::Vector3d V(pt.x, pt.y, pt.z);
        Eigen::Vector3d C = N.cross(V);
 
        // Check if we have a better correspondence
        double dist = C.dot(C);
        if (dist < min_dist) {
          min_dist = dist;
          min_index = static_cast<int>(j);
        }
      }
      if (min_dist > max_distance)
        continue;
 
      corr.index_query = idx_i;
      corr.index_match = nn_indices[min_index];
      corr.distance = nn_dists[min_index]; // min_dist;
      correspondences[nr_valid_correspondences++] = corr;
    }
  }
  else {
    PointTarget pt;
 
    // Iterate over the input set of source indices
    for (const auto& idx_i : (*indices_)) {
      tree_->nearestKSearch((*input_)[idx_i], k_, nn_indices, nn_dists);
 
      // Among the K nearest neighbours find the one with minimum perpendicular distance
      // to the normal
      double min_dist = std::numeric_limits<double>::max();
 
      // Find the best correspondence
      for (std::size_t j = 0; j < nn_indices.size(); j++) {
        PointSource pt_src;
        // Copy the source data to a target PointTarget format so we can search in the
        // tree
        copyPoint((*input_)[idx_i], pt_src);
 
        // computing the distance between a point and a line in 3d.
        // Reference - http://mathworld.wolfram.com/Point-LineDistance3-Dimensional.html
        pt.x = (*target_)[nn_indices[j]].x - pt_src.x;
        pt.y = (*target_)[nn_indices[j]].y - pt_src.y;
        pt.z = (*target_)[nn_indices[j]].z - pt_src.z;
 
        const NormalT& normal = (*source_normals_)[idx_i];
        Eigen::Vector3d N(normal.normal_x, normal.normal_y, normal.normal_z);
        Eigen::Vector3d V(pt.x, pt.y, pt.z);
        Eigen::Vector3d C = N.cross(V);
 
        // Check if we have a better correspondence
        double dist = C.dot(C);
        if (dist < min_dist) {
          min_dist = dist;
          min_index = static_cast<int>(j);
        }
      }
      if (min_dist > max_distance)
        continue;
 
      corr.index_query = idx_i;
      corr.index_match = nn_indices[min_index];
      corr.distance = nn_dists[min_index]; // min_dist;
      correspondences[nr_valid_correspondences++] = corr;
    }
  }
  correspondences.resize(nr_valid_correspondences);
  deinitCompute();
}
 
template <typename PointSource, typename PointTarget, typename NormalT, typename Scalar>
void
CorrespondenceEstimationNormalShooting<PointSource, PointTarget, NormalT, Scalar>::
    determineReciprocalCorrespondences(pcl::Correspondences& correspondences,
                                       double max_distance)
{
  if (!initCompute())
    return;
 
  // setup tree for reciprocal search
  // Set the internal point representation of choice
  if (!initComputeReciprocal())
    return;
 
  correspondences.resize(indices_->size());
 
  std::vector<int> nn_indices(k_);
  std::vector<float> nn_dists(k_);
  std::vector<int> index_reciprocal(1);
  std::vector<float> distance_reciprocal(1);
 
  int min_index = 0;
 
  pcl::Correspondence corr;
  unsigned int nr_valid_correspondences = 0;
  int target_idx = 0;
 
  // Check if the template types are the same. If true, avoid a copy.
  // Both point types MUST be registered using the POINT_CLOUD_REGISTER_POINT_STRUCT
  // macro!
  if (isSamePointType<PointSource, PointTarget>()) {
    PointTarget pt;
    // Iterate over the input set of source indices
    for (const auto& idx_i : (*indices_)) {
      tree_->nearestKSearch((*input_)[idx_i], k_, nn_indices, nn_dists);
 
      // Among the K nearest neighbours find the one with minimum perpendicular distance
      // to the normal
      double min_dist = std::numeric_limits<double>::max();
 
      // Find the best correspondence
      for (std::size_t j = 0; j < nn_indices.size(); j++) {
        // computing the distance between a point and a line in 3d.
        // Reference - http://mathworld.wolfram.com/Point-LineDistance3-Dimensional.html
        pt.x = (*target_)[nn_indices[j]].x - (*input_)[idx_i].x;
        pt.y = (*target_)[nn_indices[j]].y - (*input_)[idx_i].y;
        pt.z = (*target_)[nn_indices[j]].z - (*input_)[idx_i].z;
 
        const NormalT& normal = (*source_normals_)[idx_i];
        Eigen::Vector3d N(normal.normal_x, normal.normal_y, normal.normal_z);
        Eigen::Vector3d V(pt.x, pt.y, pt.z);
        Eigen::Vector3d C = N.cross(V);
 
        // Check if we have a better correspondence
        double dist = C.dot(C);
        if (dist < min_dist) {
          min_dist = dist;
          min_index = static_cast<int>(j);
        }
      }
      if (min_dist > max_distance)
        continue;
 
      // Check if the correspondence is reciprocal
      target_idx = nn_indices[min_index];
      tree_reciprocal_->nearestKSearch(
          (*target_)[target_idx], 1, index_reciprocal, distance_reciprocal);
 
      if (idx_i != index_reciprocal[0])
        continue;
 
      // Correspondence IS reciprocal, save it and continue
      corr.index_query = idx_i;
      corr.index_match = nn_indices[min_index];
      corr.distance = nn_dists[min_index]; // min_dist;
      correspondences[nr_valid_correspondences++] = corr;
    }
  }
  else {
    PointTarget pt;
 
    // Iterate over the input set of source indices
    for (const auto& idx_i : (*indices_)) {
      tree_->nearestKSearch((*input_)[idx_i], k_, nn_indices, nn_dists);
 
      // Among the K nearest neighbours find the one with minimum perpendicular distance
      // to the normal
      double min_dist = std::numeric_limits<double>::max();
 
      // Find the best correspondence
      for (std::size_t j = 0; j < nn_indices.size(); j++) {
        PointSource pt_src;
        // Copy the source data to a target PointTarget format so we can search in the
        // tree
        copyPoint((*input_)[idx_i], pt_src);
 
        // computing the distance between a point and a line in 3d.
        // Reference - http://mathworld.wolfram.com/Point-LineDistance3-Dimensional.html
        pt.x = (*target_)[nn_indices[j]].x - pt_src.x;
        pt.y = (*target_)[nn_indices[j]].y - pt_src.y;
        pt.z = (*target_)[nn_indices[j]].z - pt_src.z;
 
        const NormalT& normal = (*source_normals_)[idx_i];
        Eigen::Vector3d N(normal.normal_x, normal.normal_y, normal.normal_z);
        Eigen::Vector3d V(pt.x, pt.y, pt.z);
        Eigen::Vector3d C = N.cross(V);
 
        // Check if we have a better correspondence
        double dist = C.dot(C);
        if (dist < min_dist) {
          min_dist = dist;
          min_index = static_cast<int>(j);
        }
      }
      if (min_dist > max_distance)
        continue;
 
      // Check if the correspondence is reciprocal
      target_idx = nn_indices[min_index];
      tree_reciprocal_->nearestKSearch(
          (*target_)[target_idx], 1, index_reciprocal, distance_reciprocal);
 
      if (idx_i != index_reciprocal[0])
        continue;
 
      // Correspondence IS reciprocal, save it and continue
      corr.index_query = idx_i;
      corr.index_match = nn_indices[min_index];
      corr.distance = nn_dists[min_index]; // min_dist;
      correspondences[nr_valid_correspondences++] = corr;
    }
  }
  correspondences.resize(nr_valid_correspondences);
  deinitCompute();
}
 
} // namespace registration
} // namespace pcl
 
#endif // PCL_REGISTRATION_IMPL_CORRESPONDENCE_ESTIMATION_NORMAL_SHOOTING_H_