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

Yue, Shigang and Rind, F. C. (2006) Collision detection in complex dynamic scenes using an LGMD-based visual neural network with feature enhancement. IEEE transactions on neural networks, 17 (3). pp. 705-716. ISSN 1045-9227

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

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.

Item Type:Article
Additional Information: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.
Keywords:Lobula giant movement detector (LGMD), Robotics, Neural network
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
H Engineering > H671 Robotics
Divisions:College of Science > School of Computer Science
ID Code:1219
Deposited By: Jill Partridge
Deposited On:24 Sep 2007
Last Modified:18 Jul 2011 16:17

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