rmsac.h

/*
 * Software License Agreement (BSD License)
 *
 *  Point Cloud Library (PCL) - www.pointclouds.org
 *  Copyright (c) 2009, Willow Garage, Inc.
 *  Copyright (c) 2012-, Open Perception, Inc.
 *
 *  All rights reserved.
 *
 *  Redistribution and use in source and binary forms, with or without
 *  modification, are permitted provided that the following conditions
 *  are met:
 *
 *   * Redistributions of source code must retain the above copyright
 *     notice, this list of conditions and the following disclaimer.
 *   * Redistributions in binary form must reproduce the above
 *     copyright notice, this list of conditions and the following
 *     disclaimer in the documentation and/or other materials provided
 *     with the distribution.
 *   * Neither the name of the copyright holder(s) nor the names of its
 *     contributors may be used to endorse or promote products derived
 *     from this software without specific prior written permission.
 *
 *  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
 *  "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
 *  LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
 *  FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
 *  COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
 *  INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
 *  BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
 *  LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
 *  CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
 *  LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
 *  ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
 *  POSSIBILITY OF SUCH DAMAGE.
 *
 * $Id$
 *
 */
 
#pragma once
 
#include <pcl/sample_consensus/sac.h>
#include <pcl/sample_consensus/sac_model.h>
 
namespace pcl
{
  /** \brief @b RandomizedMEstimatorSampleConsensus represents an implementation of the RMSAC (Randomized M-estimator 
    * SAmple Consensus) algorithm, which basically adds a Td,d test (see \a RandomizedRandomSampleConsensus) to an MSAC 
    * estimator (see \a MEstimatorSampleConsensus).
    * \note RMSAC is useful in situations where most of the data samples belong to the model, and a fast outlier rejection algorithm is needed.
    * \author Radu B. Rusu
    * \ingroup sample_consensus
    */
  template <typename PointT>
  class RandomizedMEstimatorSampleConsensus : public SampleConsensus<PointT>
  {
    using SampleConsensusModelPtr = typename SampleConsensusModel<PointT>::Ptr;
 
    public:
      using Ptr = shared_ptr<RandomizedMEstimatorSampleConsensus<PointT> >;
      using ConstPtr = shared_ptr<const RandomizedMEstimatorSampleConsensus<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 RMSAC (Randomized M-estimator SAmple Consensus) main constructor
        * \param[in] model a Sample Consensus model
        */
      RandomizedMEstimatorSampleConsensus (const SampleConsensusModelPtr &model) 
        : SampleConsensus<PointT> (model)
        , fraction_nr_pretest_ (-1.0)
        , nr_samples_pretest_ (1)
      {
        // Maximum number of trials before we give up.
        max_iterations_ = 10000;
      }
 
      /** \brief RMSAC (Randomized M-estimator SAmple Consensus) main constructor
        * \param[in] model a Sample Consensus model
        * \param[in] threshold distance to model threshold
        */
      RandomizedMEstimatorSampleConsensus (const SampleConsensusModelPtr &model, double threshold) 
        : SampleConsensus<PointT> (model, threshold)
        , fraction_nr_pretest_ (-1.0)
        , nr_samples_pretest_ (1)
      {
        // 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;
 
      /** \brief Set the percentage of points to pre-test.
        * This is an alternative to setNrSamplesPretest.
        * \param[in] nr_pretest percentage of points to pre-test
        */
      inline void 
      setFractionNrPretest (double nr_pretest)
      {
        fraction_nr_pretest_ = nr_pretest;
        nr_samples_pretest_ = 0;
      }
 
      /** \brief Get the percentage of points to pre-test. */
      inline double 
      getFractionNrPretest () const { return (fraction_nr_pretest_); }
 
      /** \brief Set the absolute number of points to pre-test.
        * This is an alternative to setFractionNrPretest.
        * \param[in] nr_pretest absolute number of points to pre-test
        */
      inline void
      setNrSamplesPretest (std::size_t nr_pretest)
      {
        nr_samples_pretest_ = nr_pretest;
        fraction_nr_pretest_ = -1.0;
      }
 
      /** \brief Get the absolute number of points to pre-test. */
      inline std::size_t
      getNrSamplesPretest () const { return (nr_samples_pretest_); }
 
    private:
      /** \brief Number of samples to randomly pre-test, in percents. This is an alternative and mutually exclusive to nr_samples_pretest_. */
      double fraction_nr_pretest_;
 
      /** \brief Absolute number of samples to randomly pre-test. This is an alternative and mutually exclusive to fraction_nr_pretest_. */
      std::size_t nr_samples_pretest_;
  };
}
 
#ifdef PCL_NO_PRECOMPILE
#include <pcl/sample_consensus/impl/rmsac.hpp>
#endif