Point Cloud Library (PCL)  1.7.0
/tmp/buildd/pcl-1.7-1.7.0/filters/filters.doxy
00001 /**
00002   \addtogroup filters Module filters
00003 
00004   \section secFiltersPresentation Overview
00005 
00006 The <b>pcl_filters</b> library contains outlier and noise removal mechanisms
00007 for 3D point cloud data filtering applications.
00008 
00009 An example of noise removal is presented in the figure below. Due to
00010 measurement errors, certain datasets present a large number of <i>shadow
00011 points</i>. This complicates the estimation of local point cloud 3D features.
00012 Some of these outliers can be filtered by performing a statistical analysis on
00013 each point's neighborhood, and trimming those which do not meet a certain
00014 criteria. The sparse outlier removal implementation in PCL is based on the
00015 computation of the distribution of point to neighbors distances in the input
00016 dataset. For each point, the mean distance from it to all its neighbors is
00017 computed. By assuming that the resulted distribution is Gaussian with a mean
00018 and a standard deviation, all points whose mean distances are outside an
00019 interval defined by the global distances mean and standard deviation can be
00020 considered as outliers and trimmed from the dataset.
00021 
00022 \image html http://www.pointclouds.org/assets/images/contents/documentation/filters_statistical_noise.png
00023 
00024   
00025   \section secFiltersRequirements Requirements
00026   - \ref common "common"
00027   - \ref sample_consensus "sample_consensus"
00028   - \ref search "search"
00029   - \ref kdtree "kdtree"
00030   - \ref octree "octree"
00031 
00032 */