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This paper presents a method to reduce the data collected by a 3D laser range sensor. The complete point cloud consisting of several thousand points is hard to process on-line and in real-time on a robot. Similar to navigation tasks, the reduction of these points to a meaningful set is needed for further processes of object recognition. This method combines the data from a 3D laser sensor with an existing 2D map in order to reduce mapped feature points from the raw data. The main problem is the computational complexity of considering the different noise sources. The functionality of our approach is demonstrated by experiments for on-line reduction of the 3D data in indoor and outdoor environments.

DYNAMIC REAL-TIME REDUCTION OF MAPPED FEATURES IN A 3D POINT CLOUD