Deep belief net learning in a long-range vision system for autonomous off-road driving

Raia Hadsell, Ayse Erkan, Pierre Sermanet, Marco Scoffier, Urs Muller, Yann LeCun

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

Abstract

We present a learning-based approach for longrange vision that is able to accurately classify complex terrain at distances up to the horizon, thus allowing high-level strategic planning. A deep belief network is trained with unsupervised data and a reconstruction criterion to extract features from an input image, and the features are used to train a realtime classifier to predict traversability. The online supervision is given by a stereo module that provides robust labels for nearby areas up to 12 meters distant. The approach was developed and tested on the LAGR mobile robot.

Original languageEnglish (US)
Title of host publication2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS
Pages628-633
Number of pages6
DOIs
StatePublished - 2008
Event2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS - Nice, France
Duration: Sep 22 2008Sep 26 2008

Publication series

Name2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS

Other

Other2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS
CountryFrance
CityNice
Period9/22/089/26/08

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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