pcl小知识（十一）——对StatisticalOutlierRemoval的理解

转载自：https://www.cnblogs.com/zhaobinyouth/p/6229954.html

源代码：

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#ifndef PCL_FILTERS_IMPL_STATISTICAL_OUTLIER_REMOVAL_H_
#define PCL_FILTERS_IMPL_STATISTICAL_OUTLIER_REMOVAL_H_

#include <pcl/filters/statistical_outlier_removal.h>
#include <pcl/common/io.h>

////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointT> void
pcl::StatisticalOutlierRemoval<PointT>::applyFilter (PointCloud &output)
{
  std::vector<int> indices;
  if (keep_organized_)
  {
    bool temp = extract_removed_indices_;
    extract_removed_indices_ = true;
    applyFilterIndices (indices);
    extract_removed_indices_ = temp;

    output = *input_;
    for (int rii = 0; rii < static_cast<int> (removed_indices_->size ()); ++rii)  // rii = removed indices iterator
      output.points[(*removed_indices_)[rii]].x = output.points[(*removed_indices_)[rii]].y = output.points[(*removed_indices_)[rii]].z = user_filter_value_;
    if (!pcl_isfinite (user_filter_value_))
      output.is_dense = false;
  }
  else
  {
    applyFilterIndices (indices);
    copyPointCloud (*input_, indices, output);
  }
}

////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointT> void
pcl::StatisticalOutlierRemoval<PointT>::applyFilterIndices (std::vector<int> &indices)
{
  // Initialize the search class
  if (!searcher_)
  {
    if (input_->isOrganized ())
      searcher_.reset (new pcl::search::OrganizedNeighbor<PointT> ());
    else
      searcher_.reset (new pcl::search::KdTree<PointT> (false));
  }
  searcher_->setInputCloud (input_);

  // The arrays to be used
  std::vector<int> nn_indices (mean_k_);
  std::vector<float> nn_dists (mean_k_);
  std::vector<float> distances (indices_->size ());
  indices.resize (indices_->size ());
  removed_indices_->resize (indices_->size ());
  int oii = 0, rii = 0;  // oii = output indices iterator, rii = removed indices iterator

  // First pass: Compute the mean distances for all points with respect to their k nearest neighbors
  int valid_distances = 0;
  for (int iii = 0; iii < static_cast<int> (indices_->size ()); ++iii)  // iii = input indices iterator
  {
    if (!pcl_isfinite (input_->points[(*indices_)[iii]].x) ||
        !pcl_isfinite (input_->points[(*indices_)[iii]].y) ||
        !pcl_isfinite (input_->points[(*indices_)[iii]].z))
    {
      distances[iii] = 0.0;
      continue;
    }

    // Perform the nearest k search
    if (searcher_->nearestKSearch ((*indices_)[iii], mean_k_ + 1, nn_indices, nn_dists) == 0)
    {
      distances[iii] = 0.0;
      PCL_WARN ("[pcl::%s::applyFilter] Searching for the closest %d neighbors failed.\n", getClassName ().c_str (), mean_k_);
      continue;
    }

    // Calculate the mean distance to its neighbors
    double dist_sum = 0.0;
    for (int k = 1; k < mean_k_ + 1; ++k)  // k = 0 is the query point
      dist_sum += sqrt (nn_dists[k]);
    distances[iii] = static_cast<float> (dist_sum / mean_k_);
    valid_distances++;
  }

  // Estimate the mean and the standard deviation of the distance vector
  double sum = 0, sq_sum = 0;
  for (size_t i = 0; i < distances.size (); ++i)
  {
    sum += distances[i];
    sq_sum += distances[i] * distances[i];
  }
  double mean = sum / static_cast<double>(valid_distances);
  double variance = (sq_sum - sum * sum / static_cast<double>(valid_distances)) / (static_cast<double>(valid_distances) - 1);
  double stddev = sqrt (variance);
  //getMeanStd (distances, mean, stddev);

  double distance_threshold = mean + std_mul_ * stddev;

  // Second pass: Classify the points on the computed distance threshold
  for (int iii = 0; iii < static_cast<int> (indices_->size ()); ++iii)  // iii = input indices iterator
  {
    // Points having a too high average distance are outliers and are passed to removed indices
    // Unless negative was set, then it's the opposite condition
    if ((!negative_ && distances[iii] > distance_threshold) || (negative_ && distances[iii] <= distance_threshold))
    {
      if (extract_removed_indices_)
        (*removed_indices_)[rii++] = (*indices_)[iii];
      continue;
    }

    // Otherwise it was a normal point for output (inlier)
    indices[oii++] = (*indices_)[iii];
  }

  // Resize the output arrays
  indices.resize (oii);
  removed_indices_->resize (rii);
}

#define PCL_INSTANTIATE_StatisticalOutlierRemoval(T) template class PCL_EXPORTS pcl::StatisticalOutlierRemoval<T>;

#endif  // PCL_FILTERS_IMPL_STATISTICAL_OUTLIER_REMOVAL_H_

最终会执行的代码：

template <typename PointT> void
pcl::StatisticalOutlierRemoval<PointT>::applyFilterIndices (std::vector<int> &indices)

1、进行一些简单Initialize

// Initialize the search class
  if (!searcher_)
  {
    if (input_->isOrganized ())
      searcher_.reset (new pcl::search::OrganizedNeighbor<PointT> ());
    else
      searcher_.reset (new pcl::search::KdTree<PointT> (false));
  }
  searcher_->setInputCloud (input_);

2、定义一些变量

// The arrays to be used
  std::vector<int> nn_indices (mean_k_);//搜索完邻域点对应的索引
  std::vector<float> nn_dists (mean_k_);//搜索完的每个邻域点与查询点之间的欧式距离
  std::vector<float> distances (indices_->size ());
  indices.resize (indices_->size ());
  removed_indices_->resize (indices_->size ());
  int oii = 0, rii = 0;  // oii = output indices iterator, rii = removed indices iterator

3、求每个点的k邻域的均值

// First pass: Compute the mean distances for all points with respect to their k nearest neighbors
  int valid_distances = 0;
  for (int iii = 0; iii < static_cast<int> (indices_->size ()); ++iii)  // iii = input indices iterator
  {
    if (!pcl_isfinite (input_->points[(*indices_)[iii]].x) ||
        !pcl_isfinite (input_->points[(*indices_)[iii]].y) ||
        !pcl_isfinite (input_->points[(*indices_)[iii]].z))
    {
      distances[iii] = 0.0;
      continue;
    }

    // Perform the nearest k search
    if (searcher_->nearestKSearch ((*indices_)[iii], mean_k_ + 1, nn_indices, nn_dists) == 0)
    {
      distances[iii] = 0.0;
      PCL_WARN ("[pcl::%s::applyFilter] Searching for the closest %d neighbors failed.\n", getClassName ().c_str (), mean_k_);
      continue;
    }

    // Calculate the mean distance to its neighbors
    double dist_sum = 0.0;
    for (int k = 1; k < mean_k_ + 1; ++k)  // k = 0 is the query point
      dist_sum += sqrt (nn_dists[k]);
    distances[iii] = static_cast<float> (dist_sum / mean_k_);//每个点都对应了一个距离变量
    valid_distances++;
  }

4、估计距离的均值和标准差不是邻域，是根据整个数据中的点均值和标准差

// Estimate the mean and the standard deviation of the distance vector
  double sum = 0, sq_sum = 0;
  for (size_t i = 0; i < distances.size (); ++i)
  {
    sum += distances[i];
    sq_sum += distances[i] * distances[i];
  }

   double mean = sum / static_cast<double>(valid_distances);
   double variance = (sq_sum - sum * sum / static_cast<double>(valid_distances)) / (static_cast<double>    (valid_distances) - 1);
    double stddev = sqrt (variance);
   //getMeanStd (distances, mean, stddev);

5、根据设定的距离阈值与distances[iii]比较，超出设定阈值则该点被标记为离群点，并将其移除。

double distance_threshold = mean + std_mul_ * stddev;

  // Second pass: Classify the points on the computed distance threshold
  for (int iii = 0; iii < static_cast<int> (indices_->size ()); ++iii)  // iii = input indices iterator
  {
    // Points having a too high average distance are outliers and are passed to removed indices
    // Unless negative was set, then it's the opposite condition
    if ((!negative_ && distances[iii] > distance_threshold) || (negative_ && distances[iii] <= distance_threshold))
    {
      if (extract_removed_indices_)
        (*removed_indices_)[rii++] = (*indices_)[iii];
      continue;
    }

    // Otherwise it was a normal point for output (inlier)
    indices[oii++] = (*indices_)[iii];
  }
    indices.resize (oii);
    removed_indices_->resize (rii);// Resize the output arrays

pcl小知识（十一）——对StatisticalOutlierRemoval的理解

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