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



We present a learning-based approach for long-range 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.


Raia Hadsell
Ayse Naz Erkan
Pierre Sermanet
M Scoffier
U Muller
Yann LeCun