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Collision detection in complex dynamic scenes using an LGMD-based visual neural network with feature enhancement

Lookup NU author(s): Dr Claire RindORCiD

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Abstract

The lobula giant movement detector (LGMD) is an identified neuron in the locust brain that responds most strongly to the images of an approaching object such as a predator. Its computational model can cope with unpredictable environments without using specific object recognition algorithms. In this paper, an LGMD-based neural network is proposed with a new feature enhancement mechanism to enhance the expanded edges of colliding objects via grouped excitation for collision detection with complex backgrounds. The isolated excitation caused by background detail will be filtered out by the new mechanism. Offline tests demonstrated the advantages of the presented LGMD-based neural network in complex backgrounds. Real time robotics experiments using the LGMD-based neural network as the only sensory system showed that the system worked reliably in a wide range of conditions; in particular, the robot was able to navigate in arenas with structured surrounds and complex backgrounds. © 2006 IEEE.

Publication metadata

Author(s): Yue S, Rind FC

Publication type: Article

Publication status: Published

Journal: IEEE Transactions on Neural Networks

Year: 2006

Volume: 17

Issue: 3

Pages: 705-716

ISSN (print): 1045-9227

ISSN (electronic): 1941-0093

Publisher: IEEE

URL: http://dx.doi.org/10.1109/TNN.2006.873286

DOI: 10.1109/TNN.2006.873286

PubMed id: 16722174

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