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Proceedings Paper

Real-time adaptive off-road vehicle navigation and terrain classification
Author(s): Urs A. Muller; Lawrence D. Jackel; Yann LeCun; Beat Flepp
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Paper Abstract

We are developing a complete, self-contained autonomous navigation system for mobile robots that learns quickly, uses commodity components, and has the added benefit of emitting no radiation signature. It builds on the au­tonomous navigation technology developed by Net-Scale and New York University during the Defense Advanced Research Projects Agency (DARPA) Learning Applied to Ground Robots (LAGR) program and takes advantage of recent scientific advancements achieved during the DARPA Deep Learning program. In this paper we will present our approach and algorithms, show results from our vision system, discuss lessons learned from the past, and present our plans for further advancing vehicle autonomy.

Paper Details

Date Published: 17 May 2013
PDF: 19 pages
Proc. SPIE 8741, Unmanned Systems Technology XV, 87410A (17 May 2013); doi: 10.1117/12.2015533
Show Author Affiliations
Urs A. Muller, Net-Scale Technologies, Inc. (United States)
Lawrence D. Jackel, North-C Technologies, Inc. (United States)
Yann LeCun, New York Univ. (United States)
Beat Flepp, Net-Scale Technologies, Inc. (United States)


Published in SPIE Proceedings Vol. 8741:
Unmanned Systems Technology XV
Robert E. Karlsen; Douglas W. Gage; Charles M. Shoemaker; Grant R. Gerhart, Editor(s)

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