Deep Belief Net Learning in a Long-Range Vision System for Autonomous Off-Road Driving

被引：46

作者：

Hadsell, Raia ^{[1
]}

Erkan, Ayse ^{[1
]}

Sermanet, Pierre ^{[1
,2
]}

Scoffier, Marco ^{[2
]}

Muller, Urs ^{[2
]}

LeCun, Yann ^{[1
]}

机构：

[1] NYU, Courant Inst Math Sci, New York, NY 10003 USA

[2] Net Scale Technol, Morganville, NJ USA

来源：

2008 IEEE/RSJ INTERNATIONAL CONFERENCE ON ROBOTS AND INTELLIGENT SYSTEMS, VOLS 1-3, CONFERENCE PROCEEDINGS | 2008年

关键词：

D O I：

10.1109/IROS.2008.4651217

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：628 / 633

页数：6

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