mlesac.hpp

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#ifndef PCL_SAMPLE_CONSENSUS_IMPL_MLESAC_H_
#define PCL_SAMPLE_CONSENSUS_IMPL_MLESAC_H_
 
#include <limits>
#include <pcl/sample_consensus/mlesac.h>
#include <pcl/common/common.h> // for computeMedian
 
//////////////////////////////////////////////////////////////////////////
template <typename PointT> bool
pcl::MaximumLikelihoodSampleConsensus<PointT>::computeModel (int debug_verbosity_level)
{
  // Warn and exit if no threshold was set
  if (threshold_ == std::numeric_limits<double>::max())
  {
    PCL_ERROR ("[pcl::MaximumLikelihoodSampleConsensus::computeModel] No threshold set!\n");
    return (false);
  }
 
  iterations_ = 0;
  double d_best_penalty = std::numeric_limits<double>::max();
  double k = 1.0;
 
  const double log_probability  = std::log (1.0 - probability_);
  const double one_over_indices = 1.0 / static_cast<double> (sac_model_->getIndices ()->size ());
 
  Indices selection;
  Eigen::VectorXf model_coefficients (sac_model_->getModelSize ());
  std::vector<double> distances;
 
  // Compute sigma - remember to set threshold_ correctly !
  sigma_ = computeMedianAbsoluteDeviation (sac_model_->getInputCloud (), sac_model_->getIndices (), threshold_);
  const double dist_scaling_factor = -1.0 / (2.0 * sigma_ * sigma_); // Precompute since this does not change
  const double normalization_factor = 1.0 / (sqrt (2 * M_PI) * sigma_);
  if (debug_verbosity_level > 1)
    PCL_DEBUG ("[pcl::MaximumLikelihoodSampleConsensus::computeModel] Estimated sigma value: %f.\n", sigma_);
 
  // Compute the bounding box diagonal: V = sqrt (sum (max(pointCloud) - min(pointCloud)^2))
  Eigen::Vector4f min_pt, max_pt;
  getMinMax (sac_model_->getInputCloud (), sac_model_->getIndices (), min_pt, max_pt);
  max_pt -= min_pt;
  double v = sqrt (max_pt.dot (max_pt));
 
  int n_inliers_count = 0;
  std::size_t indices_size;
  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;
  
  // Iterate
  while (iterations_ < k && skipped_count < max_skip)
  {
    // Get X samples which satisfy the model criteria
    sac_model_->getSamples (iterations_, selection);
 
    if (selection.empty ()) break;
 
    // Search for inliers in the point cloud for the current plane model M
    if (!sac_model_->computeModelCoefficients (selection, model_coefficients))
    {
      //iterations_++;
      ++ skipped_count;
      continue;
    }
 
    // Iterate through the 3d points and calculate the distances from them to the model
    sac_model_->getDistancesToModel (model_coefficients, distances);
 
    if (distances.empty ())
    {
      //iterations_++;
      ++skipped_count;
      continue;
    }
    
    // Use Expectation-Maximization to find out the right value for d_cur_penalty
    // ---[ Initial estimate for the gamma mixing parameter = 1/2
    double gamma = 0.5;
    double p_outlier_prob = 0;
 
    indices_size = sac_model_->getIndices ()->size ();
    std::vector<double> p_inlier_prob (indices_size);
    for (int j = 0; j < iterations_EM_; ++j)
    {
      const double weighted_normalization_factor = gamma * normalization_factor;
      // Likelihood of a datum given that it is an inlier
      for (std::size_t i = 0; i < indices_size; ++i)
        p_inlier_prob[i] = weighted_normalization_factor * std::exp ( dist_scaling_factor * distances[i] * distances[i] );
 
      // Likelihood of a datum given that it is an outlier
      p_outlier_prob = (1 - gamma) / v;
 
      gamma = 0;
      for (std::size_t i = 0; i < indices_size; ++i)
        gamma += p_inlier_prob [i] / (p_inlier_prob[i] + p_outlier_prob);
      gamma /= static_cast<double>(sac_model_->getIndices ()->size ());
    }
 
    // Find the std::log likelihood of the model -L = -sum [std::log (pInlierProb + pOutlierProb)]
    double d_cur_penalty = 0;
    for (std::size_t i = 0; i < indices_size; ++i)
      d_cur_penalty += std::log (p_inlier_prob[i] + p_outlier_prob);
    d_cur_penalty = - d_cur_penalty;
 
    // Better match ?
    if (d_cur_penalty < d_best_penalty)
    {
      d_best_penalty = d_cur_penalty;
 
      // Save the current model/coefficients selection as being the best so far
      model_              = selection;
      model_coefficients_ = model_coefficients;
 
      n_inliers_count = 0;
      // Need to compute the number of inliers for this model to adapt k
      for (const double &distance : distances)
        if (distance <= 2 * sigma_)
          n_inliers_count++;
 
      // Compute the k parameter (k=std::log(z)/std::log(1-w^n))
      const double w = static_cast<double> (n_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::MaximumLikelihoodSampleConsensus::computeModel] Trial %d out of %d. Best penalty is %f.\n", iterations_, static_cast<int> (std::ceil (k)), d_best_penalty);
    if (iterations_ > max_iterations_)
    {
      if (debug_verbosity_level > 0)
        PCL_DEBUG ("[pcl::MaximumLikelihoodSampleConsensus::computeModel] MLESAC reached the maximum number of trials.\n");
      break;
    }
  }
 
  if (model_.empty ())
  {
    if (debug_verbosity_level > 0)
      PCL_DEBUG ("[pcl::MaximumLikelihoodSampleConsensus::computeModel] Unable to find a solution!\n");
    return (false);
  }
 
  // Iterate through the 3d points and calculate the distances from them to the model again
  sac_model_->getDistancesToModel (model_coefficients_, distances);
  Indices &indices = *sac_model_->getIndices ();
  if (distances.size () != indices.size ())
  {
    PCL_ERROR ("[pcl::MaximumLikelihoodSampleConsensus::computeModel] Estimated distances (%lu) differs than the normal of indices (%lu).\n", distances.size (), indices.size ());
    return (false);
  }
 
  inliers_.resize (distances.size ());
  // Get the inliers for the best model found
  n_inliers_count = 0;
  for (std::size_t i = 0; i < distances.size (); ++i)
    if (distances[i] <= 2 * sigma_)
      inliers_[n_inliers_count++] = indices[i];
 
  // Resize the inliers vector
  inliers_.resize (n_inliers_count);
 
  if (debug_verbosity_level > 0)
    PCL_DEBUG ("[pcl::MaximumLikelihoodSampleConsensus::computeModel] Model: %lu size, %d inliers.\n", model_.size (), n_inliers_count);
 
  return (true);
}
 
//////////////////////////////////////////////////////////////////////////
template <typename PointT> double
pcl::MaximumLikelihoodSampleConsensus<PointT>::computeMedianAbsoluteDeviation (
    const PointCloudConstPtr &cloud, 
    const IndicesPtr &indices,
    double sigma) const
{
  std::vector<double> distances (indices->size ());
 
  Eigen::Vector4f median;
  // median (dist (x - median (x)))
  computeMedian (cloud, indices, median);
 
  for (std::size_t i = 0; i < indices->size (); ++i)
  {
    pcl::Vector4fMapConst pt = (*cloud)[(*indices)[i]].getVector4fMap ();
    Eigen::Vector4f ptdiff = pt - median;
    ptdiff[3] = 0;
    distances[i] = ptdiff.dot (ptdiff);
  }
 
  const double result = pcl::computeMedian (distances.begin (), distances.end (), static_cast<double(*)(double)>(std::sqrt));
  return (sigma * result);
}
 
//////////////////////////////////////////////////////////////////////////
template <typename PointT> void
pcl::MaximumLikelihoodSampleConsensus<PointT>::getMinMax (
    const PointCloudConstPtr &cloud, 
    const IndicesPtr &indices,
    Eigen::Vector4f &min_p, 
    Eigen::Vector4f &max_p) const
{
  min_p.setConstant (std::numeric_limits<float>::max());
  max_p.setConstant (std::numeric_limits<float>::lowest());
  min_p[3] = max_p[3] = 0;
 
  for (std::size_t i = 0; i < indices->size (); ++i)
  {
    if ((*cloud)[(*indices)[i]].x < min_p[0]) min_p[0] = (*cloud)[(*indices)[i]].x;
    if ((*cloud)[(*indices)[i]].y < min_p[1]) min_p[1] = (*cloud)[(*indices)[i]].y;
    if ((*cloud)[(*indices)[i]].z < min_p[2]) min_p[2] = (*cloud)[(*indices)[i]].z;
 
    if ((*cloud)[(*indices)[i]].x > max_p[0]) max_p[0] = (*cloud)[(*indices)[i]].x;
    if ((*cloud)[(*indices)[i]].y > max_p[1]) max_p[1] = (*cloud)[(*indices)[i]].y;
    if ((*cloud)[(*indices)[i]].z > max_p[2]) max_p[2] = (*cloud)[(*indices)[i]].z;
  }
}
 
//////////////////////////////////////////////////////////////////////////
template <typename PointT> void
pcl::MaximumLikelihoodSampleConsensus<PointT>::computeMedian (
    const PointCloudConstPtr &cloud, 
    const IndicesPtr &indices,
    Eigen::Vector4f &median) const
{
  // Copy the values to vectors for faster sorting
  std::vector<float> x (indices->size ());
  std::vector<float> y (indices->size ());
  std::vector<float> z (indices->size ());
  for (std::size_t i = 0; i < indices->size (); ++i)
  {
    x[i] = (*cloud)[(*indices)[i]].x;
    y[i] = (*cloud)[(*indices)[i]].y;
    z[i] = (*cloud)[(*indices)[i]].z;
  }
 
  median[0] = pcl::computeMedian (x.begin(), x.end());
  median[1] = pcl::computeMedian (y.begin(), y.end());
  median[2] = pcl::computeMedian (z.begin(), z.end());
  median[3] = 0;
}
 
#define PCL_INSTANTIATE_MaximumLikelihoodSampleConsensus(T) template class PCL_EXPORTS pcl::MaximumLikelihoodSampleConsensus<T>;
 
#endif    // PCL_SAMPLE_CONSENSUS_IMPL_MLESAC_H_