Bio-inspired Collision Detector with Enhanced Selectivity for Ground Robotic Vision System

Qinbing Fu, Shigang Yue and Cheng Hu

Abstract

There are many ways of building collision-detecting systems. In this paper, we propose a novel collision selective visual neural network inspired by LGMD2 neurons in the juvenile locusts. Such collision-sensitive neuron matures early in the first-aged or even hatching locusts, and is only selective to detect looming dark objects against bright background in depth, represents swooping predators, a situation which is similar to ground robots or vehicles. However, little has been done on modeling LGMD2, let alone its potential applications in robotics and other vision-based areas. Compared to other collision detectors, our major contributions are first, enhancing the collision selectivity in a bio-inspired way, via constructing a computing efficient visual sensor, and realizing the revealed specific characteristics of LGMD2. Second, we applied the neural network to help near range path navigation of an autonomous ground miniature robot in an arena. We also examined its neural properties through systematic experiments challenged against image streams from a visual sensor of the micro-robot.

Session

Segmentation

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DOI

10.5244/C.30.6
https://dx.doi.org/10.5244/C.30.6

Citation

Qinbing Fu, Shigang Yue and Cheng Hu. Bio-inspired Collision Detector with Enhanced Selectivity for Ground Robotic Vision System. In Richard C. Wilson, Edwin R. Hancock and William A. P. Smith, editors, Proceedings of the British Machine Vision Conference (BMVC), pages 6.1-6.13. BMVA Press, September 2016.

Bibtex

        @inproceedings{BMVC2016_6,
        	title={Bio-inspired Collision Detector with Enhanced Selectivity for Ground Robotic Vision System},
        	author={Qinbing Fu, Shigang Yue and Cheng Hu},
        	year={2016},
        	month={September},
        	pages={6.1-6.13},
        	articleno={6},
        	numpages={13},
        	booktitle={Proceedings of the British Machine Vision Conference (BMVC)},
        	publisher={BMVA Press},
        	editor={Richard C. Wilson, Edwin R. Hancock and William A. P. Smith},
        	doi={10.5244/C.30.6},
        	isbn={1-901725-59-6},
        	url={https://dx.doi.org/10.5244/C.30.6}
        }