Adaptive long range vision in unstructured terrain

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

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

Abstract

A novel probabilistic online learning framework for autonomous off-road robot navigation is proposed. The system is purely vision-based and is particularly designed for predicting traversability in unknown or rapidly changing environments. It uses self-supervised learning to quickly adapt to novel terrains after processing a small number of frames, and it can recognize terrain elements such as paths, man-made structures, and natural obstacles at ranges up to 30 meters. The system is developed on the LAGR mobile robot platform and the performance is evaluated using multiple metrics, including ground truth.

Original languageEnglish (US)
Title of host publicationProceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2007
Pages2421-2426
Number of pages6
DOIs
StatePublished - 2007
Event2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2007 - San Diego, CA, United States
Duration: Oct 29 2007Nov 2 2007

Publication series

NameIEEE International Conference on Intelligent Robots and Systems

Other

Other2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2007
CountryUnited States
CitySan Diego, CA
Period10/29/0711/2/07

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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  • Cite this

    Erkan, A. N., Hadsell, R., Sermanet, P., Ben, J., Muller, U., & LeCun, Y. (2007). Adaptive long range vision in unstructured terrain. In Proceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2007 (pp. 2421-2426). [4399622] (IEEE International Conference on Intelligent Robots and Systems). https://doi.org/10.1109/IROS.2007.4399622