| Summary: | To operate autonomously in forested environments, unmanned ground vehicles (UGVs) must be able to identify the load-bearing surface of the terrain (i.e. the ground). This paper presents a novel two-stage approach for identifying ground points from 3-D point clouds sensed using LIDAR. The first stage, a local height-based filter, discards most of the non-ground points. The second stage, based on a support vector machine (SVM) classifier, operates on a set of geometrically defined features to identify which of the remaining points belong to the ground. Experimental results from two forested environments demonstrate the effectiveness of this approach.
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