template<typename PointT>
class pcl::StatisticalOutlierRemoval< PointT >

StatisticalOutlierRemoval uses point neighborhood statistics to filter outlier data.

The algorithm iterates through the entire input twice: During the first iteration it will compute the average distance that each point has to its nearest k neighbors. The value of k can be set using setMeanK(). Next, the mean and standard deviation of all these distances are computed in order to determine a distance threshold. The distance threshold will be equal to: mean + stddev_mult * stddev. The multiplier for the standard deviation can be set using setStddevMulThresh(). During the next iteration the points will be classified as inlier or outlier if their average neighbor distance is below or above this threshold respectively.
The neighbors found for each query point will be found amongst ALL points of setInputCloud(), not just those indexed by setIndices(). The setIndices() method only indexes the points that will be iterated through as search query points.

For more information:

• R. B. Rusu, Z. C. Marton, N. Blodow, M. Dolha, and M. Beetz. Towards 3D Point Cloud Based Object Maps for Household Environments Robotics and Autonomous Systems Journal (Special Issue on Semantic Knowledge), 2008.
  
  Usage example:
  pcl::StatisticalOutlierRemoval<PointType> sorfilter ( true); // Initializing with true will allow us to extract the removed indices

  sorfilter.setInputCloud (cloud_in);

  sorfilter.setMeanK (8);

  sorfilter.setStddevMulThresh (1.0);

  sorfilter.filter (*cloud_out);

  // The resulting cloud_out contains all points of cloud_in that have an average distance to their 8 nearest neighbors that is below the computed threshold

  // Using a standard deviation multiplier of 1.0 and assuming the average distances are normally distributed there is a 84.1% chance that a point will be an inlier

  indices_rem = sorfilter.getRemovedIndices ();

  // The indices_rem array indexes all points of cloud_in that are outliers

  Author

  Radu Bogdan Rusu

Definition at line 80 of file statistical_outlier_removal.h.