A Neural Network Enhanced Stereo Vision Obstacle Detection and Avoidance System for Unmanned Ground Vehicle

DOI

10.2991/cse.2013.1How to use a DOI?

Keywords

neural network; stereo vision; obstacle detection; unmanned ground vehicle.

Abstract

This paper presents a neural network enhanced stereo vision obstacle detection and avoidance system for unmanned ground vehicle. In this paper, we build a neural network to learn the mapping for the left image to the right image under the assumption of a flat road. Using the trained neural network we map the left image to the right directly and we get the left remapped image. So obstacles can be detected using correlation values between the right image and the left remapped image. With detection result the system tells the unmanned vehicle how to avoid obstacles. Our system does not require intrinsic calibration of stereo cameras and it does not perform the two IPM transforms. With neural network’s parallel processing our system reduces the computation expense and increases the real-time performance. Experimental results show that the proposed system is practicable.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Fanjun Liu
AU  - Binggang Cao
PY  - 2013/07
DA  - 2013/07
TI  - A Neural Network Enhanced Stereo Vision Obstacle Detection and Avoidance System for Unmanned Ground Vehicle
BT  - Proceedings of the 2nd International Conference on Advances in Computer Science and Engineering (CSE 2013)
PB  - Atlantis Press
SP  - 1
EP  - 4
SN  - 1951-6851
UR  - https://doi.org/10.2991/cse.2013.1
DO  - 10.2991/cse.2013.1
ID  - Liu2013/07
ER  -