Learned trafficability for UGVs: inferring geometry from imagery

Gregory S. Broten; David Mackay; Bruce Digney

doi:10.1117/12.776716

16 April 2008 Learned trafficability for UGVs: inferring geometry from imagery

Gregory S. Broten, David Mackay, Bruce Digney

Proceedings Volume 6962, Unmanned Systems Technology X; 69620F (2008) https://doi.org/10.1117/12.776716
Event: SPIE Defense and Security Symposium, 2008, Orlando, Florida, United States

Abstract

Unmanned ground vehicles (UGV) operating in outdoor environments must traverse unstructured terrain. This terrain is diverse in nature and contains natural obstacles such as rocks, brushes, berms, and low lying wet areas. Outdoor terrain is not static as it varies on a seasonal basis due to the life cycle associated with natural vegetation. Additionally, outdoor terrain may change appearance due to variations in lighting conditions that result from the Sun's relative position and from weather conditions such as clouds, fog or rain. This environmental diversity has long caused researchers considerable grief, as developing a classical terrain classification algorithm has proven to be a very difficult if not an impossible task. Researchers have skirted this problem by relying upon ranging sensors and constructing 2 ½D or, more recently, 3D world representations. Although geometrical representations have been used extensively, the low data rates associated with laser rangefinders, the unreliability of stereo vision, and the interaction between geometry and orientation estimation errors have limited the lookahead distance, thereby reducing the maximum attainable vehicle speeds. Learning from experience, in a more human like manner, promises to reduce or alleviate many of the issues posed by unstructured outdoor terrain. Defence R&D Canada (DRDC) "Learned Trafficability" program researches learning from experience. The paper presents DRDC's progress in extending a 2 ½D world representation using vision and learning from experience.

Citation Download Citation

Gregory S. Broten, David Mackay, and Bruce Digney "Learned trafficability for UGVs: inferring geometry from imagery", Proc. SPIE 6962, Unmanned Systems Technology X, 69620F (16 April 2008); https://doi.org/10.1117/12.776716

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