A multi-range vision strategy for autonomous offroad navigation

Raia Hadsell, Ayse Erkan, Pierre Sermanet, Jan Ben, Koray Kavukcuoglu, Urs Muller, Yann LeCun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Vision-based navigation and obstacle detection must be sophisticated in order to perform well in complicated and diverse terrain, but that complexity comes at the expense of increased system latency between image capture and actuator signals. Increased latency, or a longer control loop, degrades the reactivity of the robot. We present a navigational framework that uses a self-supervised, learningbased obstacle detector without paying a price in latency and reactivity. A long-range obstacle detector uses online learning to accurately see paths and obstacles at ranges up to 30 meters, while a fast, short-range obstacle detector avoids obstacles at up to 5 meters. The learning-based long-range module is discussed in detail, and field experiments are described which demonstrate the success of the overall system.

Original languageEnglish (US)
Title of host publicationProceedings of the 13th IASTED International Conference on Robotics and Applications, RA 2007 and Proceedings of the IASTED International Conference on Telematics
Pages457-463
Number of pages7
StatePublished - 2007
Event13th IASTED International Conference on Robotics and Applications, RA 2007 and Proceedings of the IASTED International Conference on Telematics - Wurzburg, Germany
Duration: Aug 29 2007Aug 31 2007

Publication series

NameProceedings of the 13th IASTED International Conference on Robotics and Applications, RA 2007 and Proceedings of the IASTED International Conference on Telematics

Other

Other13th IASTED International Conference on Robotics and Applications, RA 2007 and Proceedings of the IASTED International Conference on Telematics
CountryGermany
CityWurzburg
Period8/29/078/31/07

Keywords

  • LAGR
  • Latency
  • Learning
  • Navigation
  • Offroad
  • Vision

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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