gicp.h

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 * $Id: gicp.h 5026 2012-03-12 02:51:44Z rusu $
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#ifndef PCL_GICP_H_
#define PCL_GICP_H_

#include <pcl/registration/icp.h>
#include <pcl/registration/bfgs.h>

namespace pcl
{
  template <typename PointSource, typename PointTarget>
  class GeneralizedIterativeClosestPoint : public IterativeClosestPoint<PointSource, PointTarget>
  {
    using IterativeClosestPoint<PointSource, PointTarget>::reg_name_;
    using IterativeClosestPoint<PointSource, PointTarget>::getClassName;
    using IterativeClosestPoint<PointSource, PointTarget>::indices_;
    using IterativeClosestPoint<PointSource, PointTarget>::target_;
    using IterativeClosestPoint<PointSource, PointTarget>::input_;
    using IterativeClosestPoint<PointSource, PointTarget>::tree_;
    using IterativeClosestPoint<PointSource, PointTarget>::nr_iterations_;
    using IterativeClosestPoint<PointSource, PointTarget>::max_iterations_;
    using IterativeClosestPoint<PointSource, PointTarget>::previous_transformation_;
    using IterativeClosestPoint<PointSource, PointTarget>::final_transformation_;
    using IterativeClosestPoint<PointSource, PointTarget>::transformation_;
    using IterativeClosestPoint<PointSource, PointTarget>::transformation_epsilon_;
    using IterativeClosestPoint<PointSource, PointTarget>::converged_;
    using IterativeClosestPoint<PointSource, PointTarget>::corr_dist_threshold_;
    using IterativeClosestPoint<PointSource, PointTarget>::inlier_threshold_;
    using IterativeClosestPoint<PointSource, PointTarget>::min_number_correspondences_;
    using IterativeClosestPoint<PointSource, PointTarget>::update_visualizer_;

    typedef pcl::PointCloud<PointSource> PointCloudSource;
    typedef typename PointCloudSource::Ptr PointCloudSourcePtr;
    typedef typename PointCloudSource::ConstPtr PointCloudSourceConstPtr;

    typedef pcl::PointCloud<PointTarget> PointCloudTarget;
    typedef typename PointCloudTarget::Ptr PointCloudTargetPtr;
    typedef typename PointCloudTarget::ConstPtr PointCloudTargetConstPtr;

    typedef PointIndices::Ptr PointIndicesPtr;
    typedef PointIndices::ConstPtr PointIndicesConstPtr;

    typedef typename pcl::KdTree<PointSource> InputKdTree;
    typedef typename pcl::KdTree<PointSource>::Ptr InputKdTreePtr;
    
    typedef Eigen::Matrix<double, 6, 1> Vector6d;

    public:
      GeneralizedIterativeClosestPoint () 
        : k_correspondences_(20)
        , gicp_epsilon_(0.001)
        , rotation_epsilon_(2e-3)
        , input_covariances_(0)
        , target_covariances_(0)
        , mahalanobis_(0)
        , max_inner_iterations_(20)
      {
        min_number_correspondences_ = 4;
        reg_name_ = "GeneralizedIterativeClosestPoint";
        max_iterations_ = 200;
        transformation_epsilon_ = 5e-4;
        corr_dist_threshold_ = 5.;
        rigid_transformation_estimation_ = 
          boost::bind (&GeneralizedIterativeClosestPoint<PointSource, PointTarget>::estimateRigidTransformationBFGS, 
                       this, _1, _2, _3, _4, _5); 
        input_tree_.reset (new pcl::KdTreeFLANN<PointSource>);
      }

      inline void
      setInputCloud (const PointCloudSourceConstPtr &cloud)
      {
        if (cloud->points.empty ())
        {
          PCL_ERROR ("[pcl::%s::setInputInput] Invalid or empty point cloud dataset given!\n", getClassName ().c_str ());
          return;
        }
        PointCloudSource input = *cloud;
        // Set all the point.data[3] values to 1 to aid the rigid transformation
        for (size_t i = 0; i < input.size (); ++i)
          input[i].data[3] = 1.0;
        
        input_ = input.makeShared ();
        input_tree_->setInputCloud (input_);
        input_covariances_.reserve (input_->size ());
      }

      inline void 
      setInputTarget (const PointCloudTargetConstPtr &target)
      {
        pcl::Registration<PointSource, PointTarget>::setInputTarget(target);
        target_covariances_.reserve (target_->size ());
      }

      void
      estimateRigidTransformationBFGS (const PointCloudSource &cloud_src,
                                       const std::vector<int> &indices_src,
                                       const PointCloudTarget &cloud_tgt,
                                       const std::vector<int> &indices_tgt,
                                       Eigen::Matrix4f &transformation_matrix);
      
      inline const Eigen::Matrix3d& mahalanobis(size_t index) const
      {
        assert(index < mahalanobis_.size());
        return mahalanobis_[index];
      }

      void
      computeRDerivative(const Vector6d &x, const Eigen::Matrix3d &R, Vector6d &g) const;

      inline void 
      setRotationEpsilon (double epsilon) { rotation_epsilon_ = epsilon; }

      inline double 
      getRotationEpsilon () { return (rotation_epsilon_); }

      void
      setCorrespondenceRandomness (int k) { k_correspondences_ = k; }

      int
      getCorrespondenceRandomness () { return (k_correspondences_); }

      void
      setMaximumOptimizerIterations (int max) { max_inner_iterations_ = max; }

      int
      getMaximumOptimizerIterations () { return (max_inner_iterations_); }

    private:

      int k_correspondences_;

      double gicp_epsilon_;

      double rotation_epsilon_;

      Eigen::Matrix4f base_transformation_;

      const PointCloudSource *tmp_src_;

      const PointCloudTarget  *tmp_tgt_;

      const std::vector<int> *tmp_idx_src_;

      const std::vector<int> *tmp_idx_tgt_;

      InputKdTreePtr input_tree_;
      
      std::vector<Eigen::Matrix3d> input_covariances_;

      std::vector<Eigen::Matrix3d> target_covariances_;

      std::vector<Eigen::Matrix3d> mahalanobis_;
      
      int max_inner_iterations_;

      template<typename PointT>
      void computeCovariances(typename pcl::PointCloud<PointT>::ConstPtr cloud, 
                              const typename pcl::KdTree<PointT>::Ptr tree,
                              std::vector<Eigen::Matrix3d>& cloud_covariances);

      inline double 
      matricesInnerProd(const Eigen::MatrixXd& mat1, const Eigen::MatrixXd& mat2) const
      {
        double r = 0.;
        size_t n = mat1.rows();
        // tr(mat1^t.mat2)
        for(size_t i = 0; i < n; i++)
          for(size_t j = 0; j < n; j++)
            r += mat1 (j, i) * mat2 (i,j);
        return r;
      }

      void 
      computeTransformation (PointCloudSource &output, const Eigen::Matrix4f &guess);

      inline bool 
      searchForNeighbors (const PointSource &query, std::vector<int>& index, std::vector<float>& distance)
      {
        int k = tree_->nearestKSearch (query, 1, index, distance);
        if (k == 0)
          return (false);
        return (true);
      }

      void applyState(Eigen::Matrix4f &t, const Vector6d& x) const;
      
      struct OptimizationFunctorWithIndices : public BFGSDummyFunctor<double,6>
      {
        OptimizationFunctorWithIndices (const GeneralizedIterativeClosestPoint* gicp)
          : BFGSDummyFunctor<double,6> (), gicp_(gicp) {}
        double operator() (const Vector6d& x);
        void  df(const Vector6d &x, Vector6d &df);
        void fdf(const Vector6d &x, double &f, Vector6d &df);

        const GeneralizedIterativeClosestPoint *gicp_;
      };
      
    protected:
      boost::function<void(const pcl::PointCloud<PointSource> &cloud_src,
                           const std::vector<int> &src_indices,
                           const pcl::PointCloud<PointTarget> &cloud_tgt,
                           const std::vector<int> &tgt_indices,
                           Eigen::Matrix4f &transformation_matrix)> rigid_transformation_estimation_;
  };
}

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

#endif  //#ifndef PCL_GICP_H_