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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.
McDaniel et al. (Sat,) studied this question.