msac.h

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#pragma once
 
#include <pcl/sample_consensus/sac.h>
#include <pcl/sample_consensus/sac_model.h>
 
namespace pcl
{
  /** \brief @b MEstimatorSampleConsensus represents an implementation of the MSAC (M-estimator SAmple Consensus) 
    * algorithm, as described in: "MLESAC: A new robust estimator with application to estimating image geometry", P.H.S. 
    * Torr and A. Zisserman, Computer Vision and Image Understanding, vol 78, 2000.
    * The difference to RANSAC is how the quality of a model is computed: RANSAC counts the number of inliers, given a
    * threshold. The more inliers, the better the model is - it does not matter how close the inliers actually are to
    * the model, as long as they are within the threshold. MSAC changes this by using the sum of all point-model distances
    * as the quality measure, however outliers only add the threshold instead of their true distance. This method can lead
    * to better results compared to RANSAC.
    * \author Radu B. Rusu
    * \ingroup sample_consensus
    */
  template <typename PointT>
  class MEstimatorSampleConsensus : public SampleConsensus<PointT>
  {
    using SampleConsensusModelPtr = typename SampleConsensusModel<PointT>::Ptr;
 
    public:
      using Ptr = shared_ptr<MEstimatorSampleConsensus<PointT> >;
      using ConstPtr = shared_ptr<const MEstimatorSampleConsensus<PointT> >;
 
      using SampleConsensus<PointT>::max_iterations_;
      using SampleConsensus<PointT>::threshold_;
      using SampleConsensus<PointT>::iterations_;
      using SampleConsensus<PointT>::sac_model_;
      using SampleConsensus<PointT>::model_;
      using SampleConsensus<PointT>::model_coefficients_;
      using SampleConsensus<PointT>::inliers_;
      using SampleConsensus<PointT>::probability_;
 
      /** \brief MSAC (M-estimator SAmple Consensus) main constructor
        * \param[in] model a Sample Consensus model
        */
      MEstimatorSampleConsensus (const SampleConsensusModelPtr &model) 
        : SampleConsensus<PointT> (model)
      {
        // Maximum number of trials before we give up.
        max_iterations_ = 10000;
      }
 
      /** \brief MSAC (M-estimator SAmple Consensus) main constructor
        * \param[in] model a Sample Consensus model
        * \param[in] threshold distance to model threshold
        */
      MEstimatorSampleConsensus (const SampleConsensusModelPtr &model, double threshold) 
        : SampleConsensus<PointT> (model, threshold)
      {
        // Maximum number of trials before we give up.
        max_iterations_ = 10000;
      }
 
      /** \brief Compute the actual model and find the inliers
        * \param[in] debug_verbosity_level enable/disable on-screen debug information and set the verbosity level
        */
      bool 
      computeModel (int debug_verbosity_level = 0) override;
  };
}
 
#ifdef PCL_NO_PRECOMPILE
#include <pcl/sample_consensus/impl/msac.hpp>
#endif