covariance_sampling.h

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 * Point Cloud Library (PCL) - www.pointclouds.org
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#pragma once
 
#include <pcl/memory.h>
#include <pcl/pcl_macros.h>
#include <pcl/filters/filter_indices.h>
 
namespace pcl
{
  /** \brief Point Cloud sampling based on the 6D covariances. It selects the points such that the resulting cloud is
    * as stable as possible for being registered (against a copy of itself) with ICP. The algorithm adds points to the
    * resulting cloud incrementally, while trying to keep all the 6 eigenvalues of the covariance matrix as close to each
    * other as possible.
    * This class also comes with the \a computeConditionNumber method that returns a number which shows how stable a point
    * cloud will be when used as input for ICP (the closer the value it is to 1.0, the better).
    *
    * Based on the following publication:
    *    * "Geometrically Stable Sampling for the ICP Algorithm" - N. Gelfand, L. Ikemoto, S. Rusinkiewicz, M. Levoy
    *
    * \ingroup filters
    * \author Alexandru E. Ichim, alex.e.ichim@gmail.com
    */
  template <typename PointT, typename PointNT>
  class CovarianceSampling : public FilterIndices<PointT>
  {
      using FilterIndices<PointT>::filter_name_;
      using FilterIndices<PointT>::getClassName;
      using FilterIndices<PointT>::indices_;
      using FilterIndices<PointT>::input_;
      using FilterIndices<PointT>::initCompute;
 
      using Cloud = typename FilterIndices<PointT>::PointCloud;
      using CloudPtr = typename Cloud::Ptr;
      using CloudConstPtr = typename Cloud::ConstPtr;
      using NormalsConstPtr = typename pcl::PointCloud<PointNT>::ConstPtr;
 
    public:
      using Ptr = shared_ptr< CovarianceSampling<PointT, PointNT> >;
      using ConstPtr = shared_ptr< const CovarianceSampling<PointT, PointNT> >;
 
      /** \brief Empty constructor. */
      CovarianceSampling ()
      { filter_name_ = "CovarianceSampling"; }
 
      /** \brief Set number of indices to be sampled.
        * \param[in] samples the number of sample indices
        */
      inline void
      setNumberOfSamples (unsigned int samples)
      { num_samples_ = samples; }
 
      /** \brief Get the value of the internal \a num_samples_ parameter. */
      inline unsigned int
      getNumberOfSamples () const
      { return (num_samples_); }
 
      /** \brief Set the normals computed on the input point cloud
        * \param[in] normals the normals computed for the input cloud
        */
      inline void
      setNormals (const NormalsConstPtr &normals)
      { input_normals_ = normals; }
 
      /** \brief Get the normals computed on the input point cloud */
      inline NormalsConstPtr
      getNormals () const
      { return (input_normals_); }
 
 
 
      /** \brief Compute the condition number of the input point cloud. The condition number is the ratio between the
        * largest and smallest eigenvalues of the 6x6 covariance matrix of the cloud. The closer this number is to 1.0,
        * the more stable the cloud is for ICP registration.
        * \return the condition number
        */
      double
      computeConditionNumber ();
 
      /** \brief Compute the condition number of the input point cloud. The condition number is the ratio between the
        * largest and smallest eigenvalues of the 6x6 covariance matrix of the cloud. The closer this number is to 1.0,
        * the more stable the cloud is for ICP registration.
        * \param[in] covariance_matrix user given covariance matrix. Assumed to be self adjoint/symmetric.
        * \return the condition number
        */
      static double
      computeConditionNumber (const Eigen::Matrix<double, 6, 6> &covariance_matrix);
 
      /** \brief Computes the covariance matrix of the input cloud.
        * \param[out] covariance_matrix the computed covariance matrix.
        * \return whether the computation succeeded or not
        */
      bool
      computeCovarianceMatrix (Eigen::Matrix<double, 6, 6> &covariance_matrix);
 
    protected:
      /** \brief Number of indices that will be returned. */
      unsigned int num_samples_;
 
      /** \brief The normals computed at each point in the input cloud */
      NormalsConstPtr input_normals_;
 
      std::vector<Eigen::Vector3f, Eigen::aligned_allocator<Eigen::Vector3f> > scaled_points_;
 
      bool
      initCompute ();
 
      /** \brief Sample of point indices into a separate PointCloud
        * \param[out] output the resultant point cloud
        */
      void
      applyFilter (Cloud &output) override;
 
      /** \brief Sample of point indices
        * \param[out] indices the resultant point cloud indices
        */
      void
      applyFilter (Indices &indices) override;
 
      static bool
      sort_dot_list_function (std::pair<int, double> a,
                              std::pair<int, double> b)
      { return (a.second > b.second); }
 
    public:
      PCL_MAKE_ALIGNED_OPERATOR_NEW
  };
}
 
#ifdef PCL_NO_PRECOMPILE
#include <pcl/filters/impl/covariance_sampling.hpp>
#endif