statistical_outlier_removal.cpp

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 * $Id: statistical_outlier_removal.cpp 5026 2012-03-12 02:51:44Z rusu $
 *
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#include <pcl/impl/instantiate.hpp>
#include <pcl/point_types.h>
#include <pcl/filters/statistical_outlier_removal.h>
#include <pcl/filters/impl/statistical_outlier_removal.hpp>
#include <pcl/ros/conversions.h>

void
pcl::StatisticalOutlierRemoval<sensor_msgs::PointCloud2>::applyFilter (PointCloud2 &output)
{
  output.is_dense = true;
  // If fields x/y/z are not present, we cannot filter
  if (x_idx_ == -1 || y_idx_ == -1 || z_idx_ == -1)
  {
    PCL_ERROR ("[pcl::%s::applyFilter] Input dataset doesn't have x-y-z coordinates!\n", getClassName ().c_str ());
    output.width = output.height = 0;
    output.data.clear ();
    return;
  }

  if (std_mul_ == 0.0)
  {
    PCL_ERROR ("[pcl::%s::applyFilter] Standard deviation multipler not set!\n", getClassName ().c_str ());
    output.width = output.height = 0;
    output.data.clear ();
    return;
  }
  // Send the input dataset to the spatial locator
  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);
  pcl::fromROSMsg (*input_, *cloud);

  // Initialize the spatial locator
  if (!tree_)
  {
    if (cloud->isOrganized ())
      tree_.reset (new pcl::search::OrganizedNeighbor<pcl::PointXYZ> ());
    else
      tree_.reset (new pcl::search::KdTree<pcl::PointXYZ> (false));
  }

  tree_->setInputCloud (cloud);

  // Allocate enough space to hold the results
  std::vector<int> nn_indices (mean_k_);
  std::vector<float> nn_dists (mean_k_);

  std::vector<float> distances (indices_->size ());
  // Go over all the points and calculate the mean or smallest distance
  for (size_t cp = 0; cp < indices_->size (); ++cp)
  {
    if (!pcl_isfinite (cloud->points[(*indices_)[cp]].x) || !pcl_isfinite (cloud->points[(*indices_)[cp]].y)
        ||
        !pcl_isfinite (cloud->points[(*indices_)[cp]].z))
    {
      distances[cp] = 0;
      continue;
    }

    if (tree_->nearestKSearch ((*indices_)[cp], mean_k_, nn_indices, nn_dists) == 0)
    {
      distances[cp] = 0;
      PCL_WARN ("[pcl::%s::applyFilter] Searching for the closest %d neighbors failed.\n", getClassName ().c_str (), mean_k_);
      continue;
    }

    // Minimum distance (if mean_k_ == 2) or mean distance
    double dist_sum = 0;
    for (int j = 1; j < mean_k_; ++j)
      dist_sum += sqrt (nn_dists[j]);
    distances[cp] = static_cast<float> (dist_sum / (mean_k_ - 1));
  }

  // Estimate the mean and the standard deviation of the distance vector
  double mean, stddev;
  getMeanStd (distances, mean, stddev);
  double distance_threshold = mean + std_mul_ * stddev; // a distance that is bigger than this signals an outlier

  // Copy the common fields
  output.is_bigendian = input_->is_bigendian;
  output.point_step = input_->point_step;
  output.height = 1;

  output.data.resize (indices_->size () * input_->point_step); // reserve enough space
  removed_indices_->resize (input_->data.size ());

  // Build a new cloud by neglecting outliers
  int nr_p = 0;
  int nr_removed_p = 0;
  for (int cp = 0; cp < static_cast<int> (indices_->size ()); ++cp)
  {
    if (negative_)
    {
      if (distances[cp] <= distance_threshold)
      {
        if (extract_removed_indices_)
        {
          (*removed_indices_)[nr_removed_p] = cp;
          nr_removed_p++;
        }
        continue;
      }
    }
    else
    {
      if (distances[cp] > distance_threshold)
      {
        if (extract_removed_indices_)
        {
          (*removed_indices_)[nr_removed_p] = cp;
          nr_removed_p++;
        }
        continue;
      }
    }

    memcpy (&output.data[nr_p * output.point_step], &input_->data[(*indices_)[cp] * output.point_step],
            output.point_step);
    nr_p++;
  }
  output.width = nr_p;
  output.data.resize (output.width * output.point_step);
  output.row_step = output.point_step * output.width;

  removed_indices_->resize (nr_removed_p);
}
// Instantiations of specific point types
PCL_INSTANTIATE(StatisticalOutlierRemoval, PCL_XYZ_POINT_TYPES)