Proceedings of the 2nd International Conference on Advances in Computer Science and Engineering (CSE 2013)

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

Authors
Fanjun Liu, Binggang Cao
Corresponding Author
Fanjun Liu
Available Online July 2013.
DOI
https://doi.org/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.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
2nd International Conference on Advances in Computer Science and Engineering (CSE 2013)
Part of series
Advances in Intelligent Systems Research
Publication Date
July 2013
ISBN
978-90786-77-70-3
DOI
https://doi.org/10.2991/cse.2013.1How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

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  - 2nd International Conference on Advances in Computer Science and Engineering (CSE 2013)
PB  - Atlantis Press
UR  - https://doi.org/10.2991/cse.2013.1
DO  - https://doi.org/10.2991/cse.2013.1
ID  - Liu2013/07
ER  -