thirdParty/PCL 1.12.0/include/pcl-1.12/pcl/registration/registration.h

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#pragma once

// PCL includes
#include <pcl/registration/correspondence_estimation.h>
#include <pcl/registration/correspondence_rejection.h>
#include <pcl/registration/transformation_estimation.h>
#include <pcl/search/kdtree.h>
#include <pcl/memory.h>
#include <pcl/pcl_base.h>
#include <pcl/pcl_macros.h>

namespace pcl {
/** \brief @b Registration represents the base registration class for general purpose,
 * ICP-like methods. \author Radu B. Rusu, Michael Dixon \ingroup registration
 */
template <typename PointSource, typename PointTarget, typename Scalar = float>
class Registration : public PCLBase<PointSource> {
public:
  using Matrix4 = Eigen::Matrix<Scalar, 4, 4>;

  // using PCLBase<PointSource>::initCompute;
  using PCLBase<PointSource>::deinitCompute;
  using PCLBase<PointSource>::input_;
  using PCLBase<PointSource>::indices_;

  using Ptr = shared_ptr<Registration<PointSource, PointTarget, Scalar>>;
  using ConstPtr = shared_ptr<const Registration<PointSource, PointTarget, Scalar>>;

  using CorrespondenceRejectorPtr = pcl::registration::CorrespondenceRejector::Ptr;
  using KdTree = pcl::search::KdTree<PointTarget>;
  using KdTreePtr = typename KdTree::Ptr;

  using KdTreeReciprocal = pcl::search::KdTree<PointSource>;
  using KdTreeReciprocalPtr = typename KdTreeReciprocal::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 PointRepresentationConstPtr = typename KdTree::PointRepresentationConstPtr;

  using TransformationEstimation = typename pcl::registration::
      TransformationEstimation<PointSource, PointTarget, Scalar>;
  using TransformationEstimationPtr = typename TransformationEstimation::Ptr;
  using TransformationEstimationConstPtr = typename TransformationEstimation::ConstPtr;

  using CorrespondenceEstimation =
      pcl::registration::CorrespondenceEstimationBase<PointSource, PointTarget, Scalar>;
  using CorrespondenceEstimationPtr = typename CorrespondenceEstimation::Ptr;
  using CorrespondenceEstimationConstPtr = typename CorrespondenceEstimation::ConstPtr;

  /** \brief The callback signature to the function updating intermediate source point
   * cloud position during it's registration to the target point cloud. \param[in]
   * cloud_src - the point cloud which will be updated to match target \param[in]
   * indices_src - a selector of points in cloud_src \param[in] cloud_tgt - the point
   * cloud we want to register against \param[in] indices_tgt - a selector of points in
   * cloud_tgt
   */
  using UpdateVisualizerCallbackSignature = void(const pcl::PointCloud<PointSource>&,
                                                 const pcl::Indices&,
                                                 const pcl::PointCloud<PointTarget>&,
                                                 const pcl::Indices&);

  /** \brief Empty constructor. */
  Registration()
  : tree_(new KdTree)
  , tree_reciprocal_(new KdTreeReciprocal)
  , nr_iterations_(0)
  , max_iterations_(10)
  , ransac_iterations_(0)
  , target_()
  , final_transformation_(Matrix4::Identity())
  , transformation_(Matrix4::Identity())
  , previous_transformation_(Matrix4::Identity())
  , transformation_epsilon_(0.0)
  , transformation_rotation_epsilon_(0.0)
  , euclidean_fitness_epsilon_(-std::numeric_limits<double>::max())
  , corr_dist_threshold_(std::sqrt(std::numeric_limits<double>::max()))
  , inlier_threshold_(0.05)
  , converged_(false)
  , min_number_correspondences_(3)
  , correspondences_(new Correspondences)
  , transformation_estimation_()
  , correspondence_estimation_()
  , target_cloud_updated_(true)
  , source_cloud_updated_(true)
  , force_no_recompute_(false)
  , force_no_recompute_reciprocal_(false)
  , point_representation_()
  {}

  /** \brief destructor. */
  ~Registration() {}

  /** \brief Provide a pointer to the transformation estimation object.
   * (e.g., SVD, point to plane etc.)
   *
   * \param[in] te is the pointer to the corresponding transformation estimation object
   *
   * Code example:
   *
   * \code
   * TransformationEstimationPointToPlaneLLS<PointXYZ, PointXYZ>::Ptr trans_lls
   *   (new TransformationEstimationPointToPlaneLLS<PointXYZ, PointXYZ>);
   * icp.setTransformationEstimation (trans_lls);
   * // or...
   * TransformationEstimationSVD<PointXYZ, PointXYZ>::Ptr trans_svd
   *   (new TransformationEstimationSVD<PointXYZ, PointXYZ>);
   * icp.setTransformationEstimation (trans_svd);
   * \endcode
   */
  void
  setTransformationEstimation(const TransformationEstimationPtr& te)
  {
    transformation_estimation_ = te;
  }

  /** \brief Provide a pointer to the correspondence estimation object.
   * (e.g., regular, reciprocal, normal shooting etc.)
   *
   * \param[in] ce is the pointer to the corresponding correspondence estimation object
   *
   * Code example:
   *
   * \code
   * CorrespondenceEstimation<PointXYZ, PointXYZ>::Ptr
   *   ce (new CorrespondenceEstimation<PointXYZ, PointXYZ>);
   * ce->setInputSource (source);
   * ce->setInputTarget (target);
   * icp.setCorrespondenceEstimation (ce);
   * // or...
   * CorrespondenceEstimationNormalShooting<PointNormal, PointNormal, PointNormal>::Ptr
   *   cens (new CorrespondenceEstimationNormalShooting<PointNormal, PointNormal>);
   * ce->setInputSource (source);
   * ce->setInputTarget (target);
   * ce->setSourceNormals (source);
   * ce->setTargetNormals (target);
   * icp.setCorrespondenceEstimation (cens);
   * \endcode
   */
  void
  setCorrespondenceEstimation(const CorrespondenceEstimationPtr& ce)
  {
    correspondence_estimation_ = ce;
  }

  /** \brief Provide a pointer to the input source
   * (e.g., the point cloud that we want to align to the target)
   *
   * \param[in] cloud the input point cloud source
   */
  virtual void
  setInputSource(const PointCloudSourceConstPtr& cloud);

  /** \brief Get a pointer to the input point cloud dataset target. */
  inline PointCloudSourceConstPtr const
  getInputSource()
  {
    return (input_);
  }

  /** \brief Provide a pointer to the input target (e.g., the point cloud that we want
   * to align the input source to) \param[in] cloud the input point cloud target
   */
  virtual inline void
  setInputTarget(const PointCloudTargetConstPtr& cloud);

  /** \brief Get a pointer to the input point cloud dataset target. */
  inline PointCloudTargetConstPtr const
  getInputTarget()
  {
    return (target_);
  }

  /** \brief Provide a pointer to the search object used to find correspondences in
   * the target cloud.
   * \param[in] tree a pointer to the spatial search object.
   * \param[in] force_no_recompute If set to true, this tree will NEVER be
   * recomputed, regardless of calls to setInputTarget. Only use if you are
   * confident that the tree will be set correctly.
   */
  inline void
  setSearchMethodTarget(const KdTreePtr& tree, bool force_no_recompute = false)
  {
    tree_ = tree;
    force_no_recompute_ = force_no_recompute;
    // Since we just set a new tree, we need to check for updates
    target_cloud_updated_ = true;
  }

  /** \brief Get a pointer to the search method used to find correspondences in the
   * target cloud. */
  inline KdTreePtr
  getSearchMethodTarget() const
  {
    return (tree_);
  }

  /** \brief Provide a pointer to the search object used to find correspondences in
   * the source cloud (usually used by reciprocal correspondence finding).
   * \param[in] tree a pointer to the spatial search object.
   * \param[in] force_no_recompute If set to true, this tree will NEVER be
   * recomputed, regardless of calls to setInputSource. Only use if you are
   * extremely confident that the tree will be set correctly.
   */
  inline void
  setSearchMethodSource(const KdTreeReciprocalPtr& tree,
                        bool force_no_recompute = false)
  {
    tree_reciprocal_ = tree;
    force_no_recompute_reciprocal_ = force_no_recompute;
    // Since we just set a new tree, we need to check for updates
    source_cloud_updated_ = true;
  }

  /** \brief Get a pointer to the search method used to find correspondences in the
   * source cloud. */
  inline KdTreeReciprocalPtr
  getSearchMethodSource() const
  {
    return (tree_reciprocal_);
  }

  /** \brief Get the final transformation matrix estimated by the registration method.
   */
  inline Matrix4
  getFinalTransformation()
  {
    return (final_transformation_);
  }

  /** \brief Get the last incremental transformation matrix estimated by the
   * registration method. */
  inline Matrix4
  getLastIncrementalTransformation()
  {
    return (transformation_);
  }

  /** \brief Set the maximum number of iterations the internal optimization should run
   * for. \param[in] nr_iterations the maximum number of iterations the internal
   * optimization should run for
   */
  inline void
  setMaximumIterations(int nr_iterations)
  {
    max_iterations_ = nr_iterations;
  }

  /** \brief Get the maximum number of iterations the internal optimization should run
   * for, as set by the user. */
  inline int
  getMaximumIterations()
  {
    return (max_iterations_);
  }

  /** \brief Set the number of iterations RANSAC should run for.
   * \param[in] ransac_iterations is the number of iterations RANSAC should run for
   */
  inline void
  setRANSACIterations(int ransac_iterations)
  {
    ransac_iterations_ = ransac_iterations;
  }

  /** \brief Get the number of iterations RANSAC should run for, as set by the user. */
  inline double
  getRANSACIterations()
  {
    return (ransac_iterations_);
  }

  /** \brief Set the inlier distance threshold for the internal RANSAC outlier rejection
   * loop.
   *
   * The method considers a point to be an inlier, if the distance between the target
   * data index and the transformed source index is smaller than the given inlier
   * distance threshold. The value is set by default to 0.05m. \param[in]
   * inlier_threshold the inlier distance threshold for the internal RANSAC outlier
   * rejection loop
   */
  inline void
  setRANSACOutlierRejectionThreshold(double inlier_threshold)
  {
    inlier_threshold_ = inlier_threshold;
  }

  /** \brief Get the inlier distance threshold for the internal outlier rejection loop
   * as set by the user. */
  inline double
  getRANSACOutlierRejectionThreshold()
  {
    return (inlier_threshold_);
  }

  /** \brief Set the maximum distance threshold between two correspondent points in
   * source <-> target. If the distance is larger than this threshold, the points will
   * be ignored in the alignment process. \param[in] distance_threshold the maximum
   * distance threshold between a point and its nearest neighbor correspondent in order
   * to be considered in the alignment process
   */
  inline void
  setMaxCorrespondenceDistance(double distance_threshold)
  {
    corr_dist_threshold_ = distance_threshold;
  }

  /** \brief Get the maximum distance threshold between two correspondent points in
   * source <-> target. If the distance is larger than this threshold, the points will
   * be ignored in the alignment process.
   */
  inline double
  getMaxCorrespondenceDistance()
  {
    return (corr_dist_threshold_);
  }

  /** \brief Set the transformation epsilon (maximum allowable translation squared
   * difference between two consecutive transformations) in order for an optimization to
   * be considered as having converged to the final solution. \param[in] epsilon the
   * transformation epsilon in order for an optimization to be considered as having
   * converged to the final solution.
   */
  inline void
  setTransformationEpsilon(double epsilon)
  {
    transformation_epsilon_ = epsilon;
  }

  /** \brief Get the transformation epsilon (maximum allowable translation squared
   * difference between two consecutive transformations) as set by the user.
   */
  inline double
  getTransformationEpsilon()
  {
    return (transformation_epsilon_);
  }

  /** \brief Set the transformation rotation epsilon (maximum allowable rotation
   * difference between two consecutive transformations) in order for an optimization to
   * be considered as having converged to the final solution. \param[in] epsilon the
   * transformation rotation epsilon in order for an optimization to be considered as
   * having converged to the final solution (epsilon is the cos(angle) in a axis-angle
   * representation).
   */
  inline void
  setTransformationRotationEpsilon(double epsilon)
  {
    transformation_rotation_epsilon_ = epsilon;
  }

  /** \brief Get the transformation rotation epsilon (maximum allowable difference
   * between two consecutive transformations) as set by the user (epsilon is the
   * cos(angle) in a axis-angle representation).
   */
  inline double
  getTransformationRotationEpsilon()
  {
    return (transformation_rotation_epsilon_);
  }

  /** \brief Set the maximum allowed Euclidean error between two consecutive steps in
   * the ICP loop, before the algorithm is considered to have converged. The error is
   * estimated as the sum of the differences between correspondences in an Euclidean
   * sense, divided by the number of correspondences. \param[in] epsilon the maximum
   * allowed distance error before the algorithm will be considered to have converged
   */
  inline void
  setEuclideanFitnessEpsilon(double epsilon)
  {
    euclidean_fitness_epsilon_ = epsilon;
  }

  /** \brief Get the maximum allowed distance error before the algorithm will be
   * considered to have converged, as set by the user. See \ref
   * setEuclideanFitnessEpsilon
   */
  inline double
  getEuclideanFitnessEpsilon()
  {
    return (euclidean_fitness_epsilon_);
  }

  /** \brief Provide a boost shared pointer to the PointRepresentation to be used when
   * comparing points \param[in] point_representation the PointRepresentation to be used
   * by the k-D tree
   */
  inline void
  setPointRepresentation(const PointRepresentationConstPtr& point_representation)
  {
    point_representation_ = point_representation;
  }

  /** \brief Register the user callback function which will be called from registration
   * thread in order to update point cloud obtained after each iteration \param[in]
   * visualizerCallback reference of the user callback function
   */
  inline bool
  registerVisualizationCallback(
      std::function<UpdateVisualizerCallbackSignature>& visualizerCallback)
  {
    if (visualizerCallback) {
      update_visualizer_ = visualizerCallback;
      return (true);
    }
    return (false);
  }

  /** \brief Obtain the Euclidean fitness score (e.g., mean of squared distances from
   * the source to the target) \param[in] max_range maximum allowable distance between a
   * point and its correspondence in the target (default: double::max)
   */
  inline double
  getFitnessScore(double max_range = std::numeric_limits<double>::max());

  /** \brief Obtain the Euclidean fitness score (e.g., mean of squared distances from
   * the source to the target) from two sets of correspondence distances (distances
   * between source and target points) \param[in] distances_a the first set of distances
   * between correspondences \param[in] distances_b the second set of distances between
   * correspondences
   */
  inline double
  getFitnessScore(const std::vector<float>& distances_a,
                  const std::vector<float>& distances_b);

  /** \brief Return the state of convergence after the last align run */
  inline bool
  hasConverged() const
  {
    return (converged_);
  }

  /** \brief Call the registration algorithm which estimates the transformation and
   * returns the transformed source (input) as \a output. \param[out] output the
   * resultant input transformed point cloud dataset
   */
  inline void
  align(PointCloudSource& output);

  /** \brief Call the registration algorithm which estimates the transformation and
   * returns the transformed source (input) as \a output. \param[out] output the
   * resultant input transformed point cloud dataset \param[in] guess the initial gross
   * estimation of the transformation
   */
  inline void
  align(PointCloudSource& output, const Matrix4& guess);

  /** \brief Abstract class get name method. */
  inline const std::string&
  getClassName() const
  {
    return (reg_name_);
  }

  /** \brief Internal computation initialization. */
  bool
  initCompute();

  /** \brief Internal computation when reciprocal lookup is needed */
  bool
  initComputeReciprocal();

  /** \brief Add a new correspondence rejector to the list
   * \param[in] rejector the new correspondence rejector to concatenate
   *
   * Code example:
   *
   * \code
   * CorrespondenceRejectorDistance rej;
   * rej.setInputCloud<PointXYZ> (keypoints_src);
   * rej.setInputTarget<PointXYZ> (keypoints_tgt);
   * rej.setMaximumDistance (1);
   * rej.setInputCorrespondences (all_correspondences);
   *
   * // or...
   *
   * \endcode
   */
  inline void
  addCorrespondenceRejector(const CorrespondenceRejectorPtr& rejector)
  {
    correspondence_rejectors_.push_back(rejector);
  }

  /** \brief Get the list of correspondence rejectors. */
  inline std::vector<CorrespondenceRejectorPtr>
  getCorrespondenceRejectors()
  {
    return (correspondence_rejectors_);
  }

  /** \brief Remove the i-th correspondence rejector in the list
   * \param[in] i the position of the correspondence rejector in the list to remove
   */
  inline bool
  removeCorrespondenceRejector(unsigned int i)
  {
    if (i >= correspondence_rejectors_.size())
      return (false);
    correspondence_rejectors_.erase(correspondence_rejectors_.begin() + i);
    return (true);
  }

  /** \brief Clear the list of correspondence rejectors. */
  inline void
  clearCorrespondenceRejectors()
  {
    correspondence_rejectors_.clear();
  }

protected:
  /** \brief The registration method name. */
  std::string reg_name_;

  /** \brief A pointer to the spatial search object. */
  KdTreePtr tree_;

  /** \brief A pointer to the spatial search object of the source. */
  KdTreeReciprocalPtr tree_reciprocal_;

  /** \brief The number of iterations the internal optimization ran for (used
   * internally). */
  int nr_iterations_;

  /** \brief The maximum number of iterations the internal optimization should run for.
   * The default value is 10.
   */
  int max_iterations_;

  /** \brief The number of iterations RANSAC should run for. */
  int ransac_iterations_;

  /** \brief The input point cloud dataset target. */
  PointCloudTargetConstPtr target_;

  /** \brief The final transformation matrix estimated by the registration method after
   * N iterations. */
  Matrix4 final_transformation_;

  /** \brief The transformation matrix estimated by the registration method. */
  Matrix4 transformation_;

  /** \brief The previous transformation matrix estimated by the registration method
   * (used internally). */
  Matrix4 previous_transformation_;

  /** \brief The maximum difference between two consecutive transformations in order to
   * consider convergence (user defined).
   */
  double transformation_epsilon_;

  /** \brief The maximum rotation difference between two consecutive transformations in
   * order to consider convergence (user defined).
   */
  double transformation_rotation_epsilon_;

  /** \brief The maximum allowed Euclidean error between two consecutive steps in the
   * ICP loop, before the algorithm is considered to have converged. The error is
   * estimated as the sum of the differences between correspondences in an Euclidean
   * sense, divided by the number of correspondences.
   */
  double euclidean_fitness_epsilon_;

  /** \brief The maximum distance threshold between two correspondent points in source
   * <-> target. If the distance is larger than this threshold, the points will be
   * ignored in the alignment process.
   */
  double corr_dist_threshold_;

  /** \brief The inlier distance threshold for the internal RANSAC outlier rejection
   * loop. The method considers a point to be an inlier, if the distance between the
   * target data index and the transformed source index is smaller than the given inlier
   * distance threshold. The default value is 0.05.
   */
  double inlier_threshold_;

  /** \brief Holds internal convergence state, given user parameters. */
  bool converged_;

  /** \brief The minimum number of correspondences that the algorithm needs before
   * attempting to estimate the transformation. The default value is 3.
   */
  int min_number_correspondences_;

  /** \brief The set of correspondences determined at this ICP step. */
  CorrespondencesPtr correspondences_;

  /** \brief A TransformationEstimation object, used to calculate the 4x4 rigid
   * transformation. */
  TransformationEstimationPtr transformation_estimation_;

  /** \brief A CorrespondenceEstimation object, used to estimate correspondences between
   * the source and the target cloud. */
  CorrespondenceEstimationPtr correspondence_estimation_;

  /** \brief The list of correspondence rejectors to use. */
  std::vector<CorrespondenceRejectorPtr> correspondence_rejectors_;

  /** \brief Variable that stores whether we have a new target cloud, meaning we need to
   * pre-process it again. This way, we avoid rebuilding the kd-tree for the target
   * cloud every time the determineCorrespondences () method is called. */
  bool target_cloud_updated_;
  /** \brief Variable that stores whether we have a new source cloud, meaning we need to
   * pre-process it again. This way, we avoid rebuilding the reciprocal kd-tree for the
   * source cloud every time the determineCorrespondences () method is called. */
  bool source_cloud_updated_;
  /** \brief A flag which, if set, means the tree operating on the target cloud
   * will never be recomputed*/
  bool force_no_recompute_;

  /** \brief A flag which, if set, means the tree operating on the source cloud
   * will never be recomputed*/
  bool force_no_recompute_reciprocal_;

  /** \brief Callback function to update intermediate source point cloud position during
   * it's registration to the target point cloud.
   */
  std::function<UpdateVisualizerCallbackSignature> update_visualizer_;

  /** \brief Search for the closest nearest neighbor of a given point.
   * \param cloud the point cloud dataset to use for nearest neighbor search
   * \param index the index of the query point
   * \param indices the resultant vector of indices representing the k-nearest neighbors
   * \param distances the resultant distances from the query point to the k-nearest
   * neighbors
   */
  inline bool
  searchForNeighbors(const PointCloudSource& cloud,
                     int index,
                     pcl::Indices& indices,
                     std::vector<float>& distances)
  {
    int k = tree_->nearestKSearch(cloud, index, 1, indices, distances);
    if (k == 0)
      return (false);
    return (true);
  }

  /** \brief Abstract transformation computation method with initial guess */
  virtual void
  computeTransformation(PointCloudSource& output, const Matrix4& guess) = 0;

private:
  /** \brief The point representation used (internal). */
  PointRepresentationConstPtr point_representation_;

  /**
   * \brief Remove from public API in favor of \ref setInputSource
   *
   * Still gives the correct result (with a warning)
   */
  void
  setInputCloud(const PointCloudSourceConstPtr& cloud) override
  {
    PCL_WARN("[pcl::registration::Registration] setInputCloud is deprecated."
             "Please use setInputSource instead.\n");
    setInputSource(cloud);
  }

public:
  PCL_MAKE_ALIGNED_OPERATOR_NEW
};
} // namespace pcl

#include <pcl/registration/impl/registration.hpp>