A generic approach to rugged terrain analysis based on fuzzy inference

In cross-country navigation, autonomous land vehicles (ALVs) must traverse harsh natural terrains, which are uneven, rough, and sloping. One of challenges is to evaluate the terrain's characteristics quantitatively so as to prepare for smooth and stable trajectory planning subsequently. In this paper, we proposed a separate-and-integrate model to analysis rugged terrains, and developed a more universal and robust untraversable regions detection method on elevation maps. When separate, we extract the necessary and sufficient terrain characteristics such as slope, roll variance and roughness from elevation maps respectively and when integrate, the fuzzy inference is applied to combine the above terrain features in order to obtain its traversability assessment and local quantitative evaluations. Experimental results show the method can accurately evaluate terrains' characters and properly classify rugged terrains, and the classification results are robust to the uncertainty and imprecision of the terrain information. And because it's based on terrains' geometry clues, the method provides a more generic framework for rugged terrain analysis.