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Interpreting laser data to allow autonomous robot navigation on paved as well as dirt roads using a fixed angle 2D laser scanner is a daunting task. This paper introduces an algorithm for terrain classification that fuses four distinctly different classifiers: raw height, step size, slope, and roughness. Input is a single 2D laser scan and output is a classification of each laser scan range reading. The range readings are classified as either returning from an obstacle (not traversable) or from traversable ground. Experimental results are shown and discussed from the implementation done with a department developed Medium Mobile Robot and tests conducted in a national park environment.

TERRAIN CLASSIFICATION FOR OUTDOOR AUTONOMOUS ROBOTS USING SINGLE 2D LASER SCANS - Robot perception for dirt road navigation