k-4pcs registration method as an extension of 4pcs

registration/include/pcl/registration/ia_kfpcs.h

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+ *  Point Cloud Library (PCL) - www.pointclouds.org
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+
+#ifndef PCL_REGISTRATION_IA_KFPCS_H_
+#define PCL_REGISTRATION_IA_KFPCS_H_
+
+#include <pcl/registration/ia_fpcs.h>
+
+namespace pcl
+{
+  namespace registration
+  {
+    /** \brief KFPCSInitialAlignment computes corresponding four point congruent sets based on keypoints
+      * as described in: "Markerless point cloud registration with keypoint-based 4-points congruent sets",
+      * Pascal Theiler, Jan Dirk Wegner, Konrad Schindler. ISPRS Annals II-5/W2, 2013. Presented at ISPRS Workshop
+      * Laser Scanning, Antalya, Turkey, 2013.
+      * \note Method has since been improved and some variations to the paper exist.
+      * \author P.W.Theiler
+      * \ingroup registration
+      */
+    template <typename PointSource, typename PointTarget, typename NormalT = pcl::Normal, typename Scalar = float>
+    class KFPCSInitialAlignment : public virtual FPCSInitialAlignment <PointSource, PointTarget, NormalT, Scalar>
+    {
+    public:
+      /** \cond */
+      typedef boost::shared_ptr <KFPCSInitialAlignment <PointSource, PointTarget, NormalT, Scalar> > Ptr;
+      typedef boost::shared_ptr <const KFPCSInitialAlignment <PointSource, PointTarget, NormalT, Scalar> > ConstPtr;
+
+      typedef pcl::PointCloud <PointSource> PointCloudSource;
+      typedef typename PointCloudSource::Ptr PointCloudSourcePtr;
+      typedef typename PointCloudSource::iterator PointCloudSourceIterator;
+
+      typedef pcl::PointCloud <PointTarget> PointCloudTarget;
+      typedef typename PointCloudTarget::Ptr PointCloudTargetPtr;
+      typedef typename PointCloudTarget::iterator PointCloudTargetIterator;
+
+      typedef pcl::registration::MatchingCandidate MatchingCandidate;
+      typedef std::vector <MatchingCandidate> MatchingCandidates;
+      /** \endcond */
+
+
+      /** \brief Constructor. */
+      KFPCSInitialAlignment ();
+
+      /** \brief Destructor. */
+      virtual ~KFPCSInitialAlignment ()
+      {};
+
+
+      /** \brief Set the upper translation threshold used for score evaluation.
+        * \param[in] upper_trl_boundary upper translation threshold
+        */
+      inline void
+      setUpperTranslationThreshold (float upper_trl_boundary)
+      {
+        upper_trl_boundary_ = upper_trl_boundary;
+      };
+
+      /** \return the upper translation threshold used for score evaluation. */
+      inline float
+      getUpperTranslationThreshold () const
+      {
+        return (upper_trl_boundary_);
+      };
+
+
+      /** \brief Set the lower translation threshold used for score evaluation.
+        * \param[in] lower_trl_boundary lower translation threshold
+        */
+      inline void
+      setLowerTranslationThreshold (float lower_trl_boundary)
+      {
+        lower_trl_boundary_ = lower_trl_boundary;
+      };
+
+      /** \return the lower translation threshold used for score evaluation. */
+      inline float
+      getLowerTranslationThreshold () const
+      {
+        return (lower_trl_boundary_);
+      };
+
+
+      /** \brief Set the weighting factor of the translation cost term. 
+        * \param[in] lambda the weighting factor of the translation cost term
+        */
+      inline void
+      setLambda (float lambda)
+      {
+        lambda_ = lambda;
+      };
+
+      /** \return the weighting factor of the translation cost term. */
+      inline float
+      getLambda () const
+      {
+        return (lambda_);
+      };
+
+
+      /** \brief Get the N best unique candidate matches according to their fitness score.
+        * The method only returns unique transformations comparing the translation
+        * and the 3D rotation to already returned transformations.
+        *
+        * \note The method may return less than N candidates, if the number of unique candidates
+        * is smaller than N
+        *
+        * \param[in] n number of best candidates to return
+        * \param[in] min_angle3d minimum 3D angle difference in radian
+        * \param[in] min_translation3d minimum 3D translation difference
+        * \param[out] candidates vector of unique candidates
+        */
+      void
+      getNBestCandidates (int n, float min_angle3d, float min_translation3d, MatchingCandidates &candidates);
+
+      /** \brief Get all unique candidate matches with fitness scores above a threshold t.
+        * The method only returns unique transformations comparing the translation
+        * and the 3D rotation to already returned transformations.
+        *
+        * \param[in] t fitness score threshold
+        * \param[in] min_angle3d minimum 3D angle difference in radian
+        * \param[in] min_translation3d minimum 3D translation difference
+        * \param[out] candidates vector of unique candidates
+        */
+      void
+      getTBestCandidates (float t, float min_angle3d, float min_translation3d, MatchingCandidates &candidates);
+
+
+    protected:
+
+      using PCLBase <PointSource>::deinitCompute;
+      using PCLBase <PointSource>::input_;
+      using PCLBase <PointSource>::indices_;
+
+      using Registration <PointSource, PointTarget, Scalar>::reg_name_;
+      using Registration <PointSource, PointTarget, Scalar>::tree_;
+      using Registration <PointSource, PointTarget, Scalar>::final_transformation_;
+      using Registration <PointSource, PointTarget, Scalar>::ransac_iterations_;
+      using Registration <PointSource, PointTarget, Scalar>::correspondences_;
+      using Registration <PointSource, PointTarget, Scalar>::converged_;
+
+      using FPCSInitialAlignment <PointSource, PointTarget, NormalT, Scalar>::delta_;
+      using FPCSInitialAlignment <PointSource, PointTarget, NormalT, Scalar>::approx_overlap_;
+      using FPCSInitialAlignment <PointSource, PointTarget, NormalT, Scalar>::max_pair_diff_;
+      using FPCSInitialAlignment <PointSource, PointTarget, NormalT, Scalar>::max_edge_diff_;
+      using FPCSInitialAlignment <PointSource, PointTarget, NormalT, Scalar>::coincidation_limit_;
+      using FPCSInitialAlignment <PointSource, PointTarget, NormalT, Scalar>::max_mse_;
+      using FPCSInitialAlignment <PointSource, PointTarget, NormalT, Scalar>::max_inlier_dist_sqr_;
+      using FPCSInitialAlignment <PointSource, PointTarget, NormalT, Scalar>::diameter_;
+      using FPCSInitialAlignment <PointSource, PointTarget, NormalT, Scalar>::normalize_delta_;
+      using FPCSInitialAlignment <PointSource, PointTarget, NormalT, Scalar>::fitness_score_;
+      using FPCSInitialAlignment <PointSource, PointTarget, NormalT, Scalar>::score_threshold_;
+      using FPCSInitialAlignment <PointSource, PointTarget, NormalT, Scalar>::linkMatchWithBase;
+      using FPCSInitialAlignment <PointSource, PointTarget, NormalT, Scalar>::validateMatch;
+
+
+      /** \brief Internal computation initialization. */
+      virtual bool
+      initCompute ();
+
+      /** \brief Method to handle current candidate matches. Here we validate and evaluate the matches w.r.t the
+        * base and store the sorted matches (together with score values and estimated transformations).
+        *
+        * \param[in] base_indices indices of base B
+        * \param[in,out] matches vector of candidate matches w.r.t the base B. The candidate matches are 
+        * reordered during this step.
+        * \param[out] candidates vector which contains the candidates matches M
+        */
+      virtual void
+      handleMatches (
+        const std::vector <int> &base_indices,
+        std::vector <std::vector <int> > &matches,
+        MatchingCandidates &candidates);
+
+      /** \brief Validate the transformation by calculating the score value after transforming the input source cloud.
+        * The resulting score is later used as the decision criteria of the best fitting match.
+        *
+        * \param[out] transformation updated orientation matrix using all inliers
+        * \param[out] fitness_score current best score
+        * \note fitness score is only updated if the score of the current transformation exceeds the input one.
+        * \return
+        * * < 0 if previous result is better than the current one (score remains)
+        * * = 0 current result is better than the previous one (score updated)
+        */
+      virtual int
+      validateTransformation (Eigen::Matrix4f &transformation, float &fitness_score);
+
+      /** \brief Final computation of best match out of vector of matches. To avoid cross thread dependencies
+        *  during parallel running, a best match for each try was calculated.
+        * \note For forwards compatibility the candidates are stored in vectors of 'vectors of size 1'.
+        * \param[in] candidates vector of candidate matches
+        */
+      virtual void
+      finalCompute (const std::vector <MatchingCandidates > &candidates);
+
+
+      /** \brief Lower boundary for translation costs calculation.
+        * \note If not set by the user, the translation costs are not used during evaluation.
+        */
+      float lower_trl_boundary_;
+
+      /** \brief Upper boundary for translation costs calculation.
+        * \note If not set by the user, it is calculated from the estimated overlap and the diameter
+        * of the point cloud.
+        */
+      float upper_trl_boundary_;
+
+      /** \brief Weighting factor for translation costs (standard = 0.5). */
+      float lambda_;
+
+
+      /** \brief Container for resulting vector of registration candidates. */
+      MatchingCandidates candidates_;
+
+      /** \brief Flag if translation score should be used in validation (internal calculation). */
+      bool use_trl_score_;
+
+      /** \brief Subset of input indices on which we evaluate candidates.
+        * To speed up the evaluation, we only use a fix number of indices defined during initialization.
+        */
+      pcl::IndicesPtr indices_validation_;
+
+    };
+  }; // namespace registration
+}; // namespace pcl 
+
+#include <pcl/registration/impl/ia_kfpcs.hpp>
+
+#endif // PCL_REGISTRATION_IA_KFPCS_H_