Learning long-range terrain classification for autonomous navigation

This paper describes a method for learning the terrain classification of long-range appearance data from short-range, stereo-based geometry, along with a map representation for utilizing this data to improve autonomous off-road navigation. The continuous, online learning method allows the system to constantly adapt to changing terrain and environmental conditions, while the polar-perspective map representation allows the system to effectively plan with stereo data at long ranges. Various evaluations of the long-range classification and improvements in system performance are described, including results from an independent third-party testing team.