rransac.hpp

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#ifndef PCL_SAMPLE_CONSENSUS_IMPL_RRANSAC_H_
#define PCL_SAMPLE_CONSENSUS_IMPL_RRANSAC_H_
 
#include <pcl/sample_consensus/rransac.h>
 
//////////////////////////////////////////////////////////////////////////
template <typename PointT> bool
pcl::RandomizedRandomSampleConsensus<PointT>::computeModel (int debug_verbosity_level)
{
  // Warn and exit if no threshold was set
  if (threshold_ == std::numeric_limits<double>::max())
  {
    PCL_ERROR ("[pcl::RandomizedRandomSampleConsensus::computeModel] No threshold set!\n");
    return (false);
  }
 
  iterations_ = 0;
  std::size_t n_best_inliers_count = 0;
  double k = std::numeric_limits<double>::max();
 
  Indices selection;
  Eigen::VectorXf model_coefficients (sac_model_->getModelSize ());
  std::set<index_t> indices_subset;
 
  const double log_probability  = std::log (1.0 - probability_);
  const double one_over_indices = 1.0 / static_cast<double> (sac_model_->getIndices ()->size ());
 
  std::size_t n_inliers_count;
  unsigned skipped_count = 0;
  // suppress infinite loops by just allowing 10 x maximum allowed iterations for invalid model parameters!
  const unsigned max_skip = max_iterations_ * 10;
 
  // Number of samples to try randomly
  const std::size_t fraction_nr_points = (fraction_nr_pretest_ < 0.0 ? nr_samples_pretest_ : pcl_lrint (static_cast<double>(sac_model_->getIndices ()->size ()) * fraction_nr_pretest_ / 100.0));
  PCL_DEBUG ("[pcl::RandomizedRandomSampleConsensus::computeModel] Using %lu points for RRANSAC pre-test.\n", fraction_nr_points);
 
  // Iterate
  while (iterations_ < k)
  {
    // Get X samples which satisfy the model criteria
    sac_model_->getSamples (iterations_, selection);
 
    if (selection.empty ())
    {
      PCL_ERROR ("[pcl::RandomizedRandomSampleConsensus::computeModel] No samples could be selected!\n");
      break;
    }
 
    // Search for inliers in the point cloud for the current plane model M
    if (!sac_model_->computeModelCoefficients (selection, model_coefficients))
    {
      //iterations_++;
      ++skipped_count;
      if (skipped_count < max_skip)
      {
        PCL_DEBUG ("[pcl::RandomizedRandomSampleConsensus::computeModel] The function computeModelCoefficients failed, so continue with next iteration.\n");
        continue;
      }
      else
      {
        PCL_DEBUG ("[pcl::RandomizedRandomSampleConsensus::computeModel] The function computeModelCoefficients failed, and RRANSAC reached the maximum number of trials.\n");
        break;
      }
    }
 
    // RRANSAC addon: verify a random fraction of the data
    // Get X random samples which satisfy the model criterion
    this->getRandomSamples (sac_model_->getIndices (), fraction_nr_points, indices_subset);
    if (!sac_model_->doSamplesVerifyModel (indices_subset, model_coefficients, threshold_))
    {
      ++iterations_;
      PCL_DEBUG ("[pcl::RandomizedRandomSampleConsensus::computeModel] The function doSamplesVerifyModel failed, so continue with next iteration.\n");
      continue;
    }
 
    // Select the inliers that are within threshold_ from the model
    n_inliers_count = sac_model_->countWithinDistance (model_coefficients, threshold_);
 
    // Better match ?
    if (n_inliers_count > n_best_inliers_count)
    {
      n_best_inliers_count = n_inliers_count;
 
      // Save the current model/inlier/coefficients selection as being the best so far
      model_              = selection;
      model_coefficients_ = model_coefficients;
 
      // Compute the k parameter (k=std::log(z)/std::log(1-w^n))
      const double w = static_cast<double> (n_best_inliers_count) * one_over_indices;
      double p_outliers = 1.0 - std::pow (w, static_cast<double> (selection.size ()));       // Probability that selection is contaminated by at least one outlier
      p_outliers = (std::max) (std::numeric_limits<double>::epsilon (), p_outliers);         // Avoid division by -Inf
      p_outliers = (std::min) (1.0 - std::numeric_limits<double>::epsilon (), p_outliers);   // Avoid division by 0.
      k = log_probability / std::log (p_outliers);
    }
 
    ++iterations_;
 
    if (debug_verbosity_level > 1)
      PCL_DEBUG ("[pcl::RandomizedRandomSampleConsensus::computeModel] Trial %d out of %d: %u inliers (best is: %u so far).\n", iterations_, static_cast<int> (std::ceil (k)), n_inliers_count, n_best_inliers_count);
    if (iterations_ > max_iterations_)
    {
      if (debug_verbosity_level > 0)
        PCL_DEBUG ("[pcl::RandomizedRandomSampleConsensus::computeModel] RRANSAC reached the maximum number of trials.\n");
      break;
    }
  }
 
  if (debug_verbosity_level > 0)
    PCL_DEBUG ("[pcl::RandomizedRandomSampleConsensus::computeModel] Model: %lu size, %u inliers.\n", model_.size (), n_best_inliers_count);
 
  if (model_.empty ())
  {
    PCL_ERROR ("[pcl::RandomizedRandomSampleConsensus::computeModel] RRANSAC found no model.\n");
    inliers_.clear ();
    return (false);
  }
 
  // Get the set of inliers that correspond to the best model found so far
  sac_model_->selectWithinDistance (model_coefficients_, threshold_, inliers_);
  return (true);
}
 
#define PCL_INSTANTIATE_RandomizedRandomSampleConsensus(T) template class PCL_EXPORTS pcl::RandomizedRandomSampleConsensus<T>;
 
#endif    // PCL_SAMPLE_CONSENSUS_IMPL_RRANSAC_H_