Online, self-supervised vision-based terrain classification in unstructured environments
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Original paper: Stereo vision with texture learning for fault-tolerant automatic baling
Computers and Electronics in Agriculture
Progress toward multi-robot reconnaissance and the MAGIC 2010 competition
Journal of Field Robotics
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The challenge in the DARPA Learning Applied to Ground Robots (LAGR) project is to autonomously navigate a small robot using stereo vision as the main sensor. During this project, we demonstrated a complete autonomous system for off-road navigation in unstructured environments, using stereo vision as the main sensor. The system is very robust—we can typically give it a goal position several hundred meters away and expect it to get there. In this paper we describe the main components that comprise the system, including stereo processing, obstacle and free space interpretation, long-range perception, online terrain traversability learning, visual odometry, map registration, planning, and control. At the end of 3 years, the system we developed outperformed all nine other teams in final blind tests over previously unseen terrain. © 2008 Wiley Periodicals, Inc.