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

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.

• Type: Conference
• Year: 2008
• Where: IROS
• Authors: Raia Hadsell, Ayse Erkan, Pierre Sermanet, Marco Scoffier, Urs Muller, Yann LeCun
• Keywords: Deep Belief Network, IROS 2008, LAGR Mobile Robot, Learning-based Approach, Robotics