correspondence_estimation_backprojection.h

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#pragma once
 
#include <pcl/registration/correspondence_estimation.h>
#include <pcl/registration/correspondence_types.h>
 
namespace pcl {
namespace registration {
/** \brief @b CorrespondenceEstimationBackprojection computes
 * correspondences as points in the target cloud which have minimum
 * \author Suat Gedikli
 * \ingroup registration
 */
template <typename PointSource,
          typename PointTarget,
          typename NormalT,
          typename Scalar = float>
class CorrespondenceEstimationBackProjection
: public CorrespondenceEstimationBase<PointSource, PointTarget, Scalar> {
public:
  using Ptr = shared_ptr<CorrespondenceEstimationBackProjection<PointSource,
                                                                PointTarget,
                                                                NormalT,
                                                                Scalar>>;
  using ConstPtr = shared_ptr<const CorrespondenceEstimationBackProjection<PointSource,
                                                                           PointTarget,
                                                                           NormalT,
                                                                           Scalar>>;
 
  using CorrespondenceEstimationBase<PointSource, PointTarget, Scalar>::initCompute;
  using CorrespondenceEstimationBase<PointSource, PointTarget, Scalar>::
      initComputeReciprocal;
  using CorrespondenceEstimationBase<PointSource, PointTarget, Scalar>::
      input_transformed_;
  using PCLBase<PointSource>::deinitCompute;
  using PCLBase<PointSource>::input_;
  using PCLBase<PointSource>::indices_;
  using CorrespondenceEstimationBase<PointSource, PointTarget, Scalar>::getClassName;
  using CorrespondenceEstimationBase<PointSource, PointTarget, Scalar>::
      point_representation_;
  using CorrespondenceEstimationBase<PointSource, PointTarget, Scalar>::target_indices_;
 
  using KdTree = pcl::search::KdTree<PointTarget>;
  using KdTreePtr = typename KdTree::Ptr;
 
  using PointCloudSource = pcl::PointCloud<PointSource>;
  using PointCloudSourcePtr = typename PointCloudSource::Ptr;
  using PointCloudSourceConstPtr = typename PointCloudSource::ConstPtr;
 
  using PointCloudTarget = pcl::PointCloud<PointTarget>;
  using PointCloudTargetPtr = typename PointCloudTarget::Ptr;
  using PointCloudTargetConstPtr = typename PointCloudTarget::ConstPtr;
 
  using PointCloudNormals = pcl::PointCloud<NormalT>;
  using NormalsPtr = typename PointCloudNormals::Ptr;
  using NormalsConstPtr = typename PointCloudNormals::ConstPtr;
 
  /** \brief Empty constructor.
   *
   * \note
   * Sets the number of neighbors to be considered in the target point cloud (k_) to 10.
   */
  CorrespondenceEstimationBackProjection()
  : source_normals_(), source_normals_transformed_(), target_normals_()
  {
    corr_name_ = "CorrespondenceEstimationBackProjection";
  }
 
  /** \brief Empty destructor */
  virtual ~CorrespondenceEstimationBackProjection() = default;
 
  /** \brief Set the normals computed on the source point cloud
   * \param[in] normals the normals computed for the source cloud
   */
  inline void
  setSourceNormals(const NormalsConstPtr& normals)
  {
    source_normals_ = normals;
  }
 
  /** \brief Get the normals of the source point cloud
   */
  inline NormalsConstPtr
  getSourceNormals() const
  {
    return (source_normals_);
  }
 
  /** \brief Set the normals computed on the target point cloud
   * \param[in] normals the normals computed for the target cloud
   */
  inline void
  setTargetNormals(const NormalsConstPtr& normals)
  {
    target_normals_ = normals;
  }
 
  /** \brief Get the normals of the target point cloud
   */
  inline NormalsConstPtr
  getTargetNormals() const
  {
    return (target_normals_);
  }
 
  /** \brief See if this rejector requires source normals */
  bool
  requiresSourceNormals() const
  {
    return (true);
  }
 
  /** \brief Blob method for setting the source normals */
  void
  setSourceNormals(pcl::PCLPointCloud2::ConstPtr cloud2)
  {
    NormalsPtr cloud(new PointCloudNormals);
    fromPCLPointCloud2(*cloud2, *cloud);
    setSourceNormals(cloud);
  }
 
  /** \brief See if this rejector requires target normals*/
  bool
  requiresTargetNormals() const
  {
    return (true);
  }
 
  /** \brief Method for setting the target normals */
  void
  setTargetNormals(pcl::PCLPointCloud2::ConstPtr cloud2)
  {
    NormalsPtr cloud(new PointCloudNormals);
    fromPCLPointCloud2(*cloud2, *cloud);
    setTargetNormals(cloud);
  }
 
  /** \brief Determine the correspondences between input and target cloud.
   * \param[out] correspondences the found correspondences (index of query point, index
   * of target point, distance) \param[in] max_distance maximum distance between the
   * normal on the source point cloud and the corresponding point in the target point
   * cloud
   */
  void
  determineCorrespondences(pcl::Correspondences& correspondences,
                           double max_distance = std::numeric_limits<double>::max());
 
  /** \brief Determine the reciprocal correspondences between input and target cloud.
   * A correspondence is considered reciprocal if both Src_i has Tgt_i as a
   * correspondence, and Tgt_i has Src_i as one.
   *
   * \param[out] correspondences the found correspondences (index of query and target
   * point, distance) \param[in] max_distance maximum allowed distance between
   * correspondences
   */
  virtual void
  determineReciprocalCorrespondences(
      pcl::Correspondences& correspondences,
      double max_distance = std::numeric_limits<double>::max());
 
  /** \brief Set the number of nearest neighbours to be considered in the target
   * point cloud. By default, we use k = 10 nearest neighbors.
   *
   * \param[in] k the number of nearest neighbours to be considered
   */
  inline void
  setKSearch(unsigned int k)
  {
    k_ = k;
  }
 
  /** \brief Get the number of nearest neighbours considered in the target point
   * cloud for computing correspondences. By default we use k = 10 nearest
   * neighbors.
   */
  inline unsigned int
  getKSearch() const
  {
    return (k_);
  }
 
  /** \brief Clone and cast to CorrespondenceEstimationBase */
  virtual typename CorrespondenceEstimationBase<PointSource, PointTarget, Scalar>::Ptr
  clone() const
  {
    Ptr copy(new CorrespondenceEstimationBackProjection<PointSource,
                                                        PointTarget,
                                                        NormalT,
                                                        Scalar>(*this));
    return (copy);
  }
 
protected:
  using CorrespondenceEstimationBase<PointSource, PointTarget, Scalar>::corr_name_;
  using CorrespondenceEstimationBase<PointSource, PointTarget, Scalar>::tree_;
  using CorrespondenceEstimationBase<PointSource, PointTarget, Scalar>::
      tree_reciprocal_;
  using CorrespondenceEstimationBase<PointSource, PointTarget, Scalar>::target_;
 
  /** \brief Internal computation initialization. */
  bool
  initCompute();
 
private:
  /** \brief The normals computed at each point in the source cloud */
  NormalsConstPtr source_normals_;
 
  /** \brief The normals computed at each point in the source cloud */
  NormalsPtr source_normals_transformed_;
 
  /** \brief The normals computed at each point in the target cloud */
  NormalsConstPtr target_normals_;
 
  /** \brief The number of neighbours to be considered in the target point cloud */
  unsigned int k_{10};
};
} // namespace registration
} // namespace pcl
 
#include <pcl/registration/impl/correspondence_estimation_backprojection.hpp>