rransac.cpp

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/*
 * Copyright (c) 2008 Radu Bogdan Rusu <rusu -=- cs.tum.edu>
 *
 * All rights reserved.
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 * modification, are permitted provided that the following conditions are met:
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 * $Id$
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#include <limits>
#include <door_handle_detector/sample_consensus/rransac.h>

namespace sample_consensus
{

  RRANSAC::RRANSAC (SACModel *model, double threshold) : SAC (model)
  {
    this->threshold_ = threshold;
    // Desired probability of choosing at least one sample free from outliers
    this->probability_    = 0.99;
    // Maximum number of trials before we give up.
    this->max_iterations_ = 10000;

    this->iterations_ = 0;

    // Number of samples to try randomly in percents
    fraction_nr_pretest_ = 10;
  }


  RRANSAC::RRANSAC (SACModel* model) : SAC (model) { }


  bool
    RRANSAC::computeModel (int debug)
  {
    iterations_ = 0;
    int n_best_inliers_count = -INT_MAX;
    double k = 1.0;

    std::vector<int> best_model;
    std::vector<int> best_inliers, inliers;
    std::vector<int> selection;

    int n_inliers_count = 0;

    // Number of samples to try randomly in percents
    int fraction_nr_points = lrint (sac_model_->getIndices ()->size () * fraction_nr_pretest_ / 100.0);

    // Iterate
    while (iterations_ < k)
    {
      // Get X samples which satisfy the model criteria
      sac_model_->getSamples (iterations_, selection);

      if (selection.size () == 0) break;

      // Search for inliers in the point cloud for the current plane model M
      sac_model_->computeModelCoefficients (selection);

      // RRANSAC addon: verify a random fraction of the data
      // Get X random samples which satisfy the model criteria
      std::set<int> fraction_idx = getRandomSamples (*sac_model_->getCloud (), *sac_model_->getIndices (), fraction_nr_points);

      if (!sac_model_->doSamplesVerifyModel (fraction_idx, threshold_))
      {
        // Unfortunately we cannot "continue" after the first iteration, because k might not be set, while iterations gets incremented
        if (k != 1.0)
        {
          iterations_ += 1;
          continue;
        }
      }

      sac_model_->selectWithinDistance (sac_model_->getModelCoefficients (), threshold_, inliers);
      n_inliers_count = inliers.size ();

      // Better match ?
      if (n_inliers_count > n_best_inliers_count)
      {
        n_best_inliers_count = n_inliers_count;
        best_inliers = inliers;
        //inliers.clear ();
        best_model = selection;

        // Compute the k parameter (k=log(z)/log(1-w^n))
        double w = (double)((double)n_inliers_count / (double)sac_model_->getIndices ()->size ());
        double p_no_outliers = 1 - pow (w, (double)selection.size ());
        p_no_outliers = std::max (std::numeric_limits<double>::epsilon (), p_no_outliers);       // Avoid division by -Inf
        p_no_outliers = std::min (1 - std::numeric_limits<double>::epsilon (), p_no_outliers);   // Avoid division by 0.
        k = log (1 - probability_) / log (p_no_outliers);
      }

      iterations_ += 1;
      if (debug > 1)
        std::cerr << "[RRANSAC::computeModel] Trial " << iterations_ << " out of " << ceil (k) << ": " << n_inliers_count << " inliers (best is: " << n_best_inliers_count << " so far)." << std::endl;
      if (iterations_ > max_iterations_)
      {
        if (debug > 0)
          std::cerr << "[RRANSAC::computeModel] RANSAC reached the maximum number of trials." << std::endl;
        break;
      }
    }

    if (best_model.size () != 0)
    {
      if (debug > 0)
        std::cerr << "[RRANSAC::computeModel] Model found: " << n_best_inliers_count << " inliers." << std::endl;
      sac_model_->setBestModel (best_model);
      sac_model_->setBestInliers (best_inliers);
      return (true);
    }
    else
      if (debug > 0)
        std::cerr << "[RRANSAC::computeModel] Unable to find a solution!" << std::endl;
    return (false);
  }


  void
    RRANSAC::setFractionNrPretest (int nr_pretest)
  {
    fraction_nr_pretest_ = nr_pretest;
  }

}