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

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#pragma once

#include <pcl/registration/correspondence_rejection_poly.h>
#include <pcl/registration/registration.h>
#include <pcl/registration/transformation_estimation_svd.h>
#include <pcl/registration/transformation_validation.h>

namespace pcl {
/** \brief Pose estimation and alignment class using a prerejective RANSAC routine.
 *
 * This class inserts a simple, yet effective "prerejection" step into the standard
 * RANSAC pose estimation loop in order to avoid verification of pose hypotheses
 * that are likely to be wrong. This is achieved by local pose-invariant geometric
 * constraints, as also implemented in the class
 * \ref registration::CorrespondenceRejectorPoly "CorrespondenceRejectorPoly".
 *
 * In order to robustly align partial/occluded models, this routine performs
 * fit error evaluation using only inliers, i.e. points closer than a
 * Euclidean threshold, which is specifiable using \ref setInlierFraction().
 *
 * The amount of prerejection or "greedyness" of the algorithm can be specified
 * using \ref setSimilarityThreshold() in [0,1[, where a value of 0 means disabled,
 * and 1 is maximally rejective.
 *
 * If you use this in academic work, please cite:
 *
 * A. G. Buch, D. Kraft, J.-K. Kämäräinen, H. G. Petersen and N. Krüger.
 * Pose Estimation using Local Structure-Specific Shape and Appearance Context.
 * International Conference on Robotics and Automation (ICRA), 2013.
 *
 * \author Anders Glent Buch (andersgb1@gmail.com)
 * \ingroup registration
 */
template <typename PointSource, typename PointTarget, typename FeatureT>
class SampleConsensusPrerejective : public Registration<PointSource, PointTarget> {
public:
  using Matrix4 = typename Registration<PointSource, PointTarget>::Matrix4;

  using Registration<PointSource, PointTarget>::reg_name_;
  using Registration<PointSource, PointTarget>::getClassName;
  using Registration<PointSource, PointTarget>::input_;
  using Registration<PointSource, PointTarget>::target_;
  using Registration<PointSource, PointTarget>::tree_;
  using Registration<PointSource, PointTarget>::max_iterations_;
  using Registration<PointSource, PointTarget>::corr_dist_threshold_;
  using Registration<PointSource, PointTarget>::transformation_;
  using Registration<PointSource, PointTarget>::final_transformation_;
  using Registration<PointSource, PointTarget>::transformation_estimation_;
  using Registration<PointSource, PointTarget>::getFitnessScore;
  using Registration<PointSource, PointTarget>::converged_;

  using PointCloudSource =
      typename Registration<PointSource, PointTarget>::PointCloudSource;
  using PointCloudSourcePtr = typename PointCloudSource::Ptr;
  using PointCloudSourceConstPtr = typename PointCloudSource::ConstPtr;

  using PointCloudTarget =
      typename Registration<PointSource, PointTarget>::PointCloudTarget;

  using PointIndicesPtr = PointIndices::Ptr;
  using PointIndicesConstPtr = PointIndices::ConstPtr;

  using FeatureCloud = pcl::PointCloud<FeatureT>;
  using FeatureCloudPtr = typename FeatureCloud::Ptr;
  using FeatureCloudConstPtr = typename FeatureCloud::ConstPtr;

  using Ptr =
      shared_ptr<SampleConsensusPrerejective<PointSource, PointTarget, FeatureT>>;
  using ConstPtr =
      shared_ptr<const SampleConsensusPrerejective<PointSource, PointTarget, FeatureT>>;

  using FeatureKdTreePtr = typename KdTreeFLANN<FeatureT>::Ptr;

  using CorrespondenceRejectorPoly =
      pcl::registration::CorrespondenceRejectorPoly<PointSource, PointTarget>;
  using CorrespondenceRejectorPolyPtr = typename CorrespondenceRejectorPoly::Ptr;
  using CorrespondenceRejectorPolyConstPtr =
      typename CorrespondenceRejectorPoly::ConstPtr;

  /** \brief Constructor */
  SampleConsensusPrerejective()
  : input_features_()
  , target_features_()
  , nr_samples_(3)
  , k_correspondences_(2)
  , feature_tree_(new pcl::KdTreeFLANN<FeatureT>)
  , correspondence_rejector_poly_(new CorrespondenceRejectorPoly)
  , inlier_fraction_(0.0f)
  {
    reg_name_ = "SampleConsensusPrerejective";
    correspondence_rejector_poly_->setSimilarityThreshold(0.6f);
    max_iterations_ = 5000;
    transformation_estimation_.reset(
        new pcl::registration::TransformationEstimationSVD<PointSource, PointTarget>);
  };

  /** \brief Destructor */
  ~SampleConsensusPrerejective() {}

  /** \brief Provide a boost shared pointer to the source point cloud's feature
   * descriptors \param features the source point cloud's features
   */
  void
  setSourceFeatures(const FeatureCloudConstPtr& features);

  /** \brief Get a pointer to the source point cloud's features */
  inline const FeatureCloudConstPtr
  getSourceFeatures() const
  {
    return (input_features_);
  }

  /** \brief Provide a boost shared pointer to the target point cloud's feature
   * descriptors \param features the target point cloud's features
   */
  void
  setTargetFeatures(const FeatureCloudConstPtr& features);

  /** \brief Get a pointer to the target point cloud's features */
  inline const FeatureCloudConstPtr
  getTargetFeatures() const
  {
    return (target_features_);
  }

  /** \brief Set the number of samples to use during each iteration
   * \param nr_samples the number of samples to use during each iteration
   */
  inline void
  setNumberOfSamples(int nr_samples)
  {
    nr_samples_ = nr_samples;
  }

  /** \brief Get the number of samples to use during each iteration, as set by the user
   */
  inline int
  getNumberOfSamples() const
  {
    return (nr_samples_);
  }

  /** \brief Set the number of neighbors to use when selecting a random feature
   * correspondence.  A higher value will add more randomness to the feature matching.
   * \param k the number of neighbors to use when selecting a random feature
   * correspondence.
   */
  inline void
  setCorrespondenceRandomness(int k)
  {
    k_correspondences_ = k;
  }

  /** \brief Get the number of neighbors used when selecting a random feature
   * correspondence, as set by the user */
  inline int
  getCorrespondenceRandomness() const
  {
    return (k_correspondences_);
  }

  /** \brief Set the similarity threshold in [0,1[ between edge lengths of the
   * underlying polygonal correspondence rejector object, where 1 is a perfect match
   * \param similarity_threshold edge length similarity threshold
   */
  inline void
  setSimilarityThreshold(float similarity_threshold)
  {
    correspondence_rejector_poly_->setSimilarityThreshold(similarity_threshold);
  }

  /** \brief Get the similarity threshold between edge lengths of the underlying
   * polygonal correspondence rejector object, \return edge length similarity threshold
   */
  inline float
  getSimilarityThreshold() const
  {
    return correspondence_rejector_poly_->getSimilarityThreshold();
  }

  /** \brief Set the required inlier fraction (of the input)
   * \param inlier_fraction required inlier fraction, must be in [0,1]
   */
  inline void
  setInlierFraction(float inlier_fraction)
  {
    inlier_fraction_ = inlier_fraction;
  }

  /** \brief Get the required inlier fraction
   * \return required inlier fraction in [0,1]
   */
  inline float
  getInlierFraction() const
  {
    return inlier_fraction_;
  }

  /** \brief Get the inlier indices of the source point cloud under the final
   * transformation
   * @return inlier indices
   */
  inline const pcl::Indices&
  getInliers() const
  {
    return inliers_;
  }

protected:
  /** \brief Choose a random index between 0 and n-1
   * \param n the number of possible indices to choose from
   */
  inline int
  getRandomIndex(int n) const
  {
    return (static_cast<int>(n * (rand() / (RAND_MAX + 1.0))));
  };

  /** \brief Select \a nr_samples sample points from cloud while making sure that their
   * pairwise distances are greater than a user-defined minimum distance, \a
   * min_sample_distance. \param cloud the input point cloud \param nr_samples the
   * number of samples to select \param sample_indices the resulting sample indices
   */
  void
  selectSamples(const PointCloudSource& cloud,
                int nr_samples,
                pcl::Indices& sample_indices);

  /** \brief For each of the sample points, find a list of points in the target cloud
   * whose features are similar to the sample points' features. From these, select one
   * randomly which will be considered that sample point's correspondence. \param
   * sample_indices the indices of each sample point \param similar_features
   * correspondence cache, which is used to read/write already computed correspondences
   * \param corresponding_indices the resulting indices of each sample's corresponding
   * point in the target cloud
   */
  void
  findSimilarFeatures(const pcl::Indices& sample_indices,
                      std::vector<pcl::Indices>& similar_features,
                      pcl::Indices& corresponding_indices);

  /** \brief Rigid transformation computation method.
   * \param output the transformed input point cloud dataset using the rigid
   * transformation found \param guess The computed transformation
   */
  void
  computeTransformation(PointCloudSource& output,
                        const Eigen::Matrix4f& guess) override;

  /** \brief Obtain the fitness of a transformation
   * The following metrics are calculated, based on
   * \b final_transformation_ and \b corr_dist_threshold_:
   *   - Inliers: the number of transformed points which are closer than threshold to NN
   *   - Error score: the MSE of the inliers
   * \param inliers indices of source point cloud inliers
   * \param fitness_score output fitness score as RMSE
   */
  void
  getFitness(pcl::Indices& inliers, float& fitness_score);

  /** \brief The source point cloud's feature descriptors. */
  FeatureCloudConstPtr input_features_;

  /** \brief The target point cloud's feature descriptors. */
  FeatureCloudConstPtr target_features_;

  /** \brief The number of samples to use during each iteration. */
  int nr_samples_;

  /** \brief The number of neighbors to use when selecting a random feature
   * correspondence. */
  int k_correspondences_;

  /** \brief The KdTree used to compare feature descriptors. */
  FeatureKdTreePtr feature_tree_;

  /** \brief The polygonal correspondence rejector used for prerejection */
  CorrespondenceRejectorPolyPtr correspondence_rejector_poly_;

  /** \brief The fraction [0,1] of inlier points required for accepting a transformation
   */
  float inlier_fraction_;

  /** \brief Inlier points of final transformation as indices into source */
  pcl::Indices inliers_;
};
} // namespace pcl

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