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Vision-based detection and tracking of aerial targets for UAV collision avoidance

By Luis Mejias, Scott McNamara, John S. Lai and Jason J. Ford

Abstract

Machine vision represents a particularly attractive solution for sensing and detecting potential collision-course targets due to the relatively low cost, size, weight, and power requirements of the sensors involved (as opposed to radar). This paper describes the development and evaluation of a vision-based collision detection algorithm suitable for fixed-wing aerial robotics. The system was evaluated using highly realistic vision data of the moments leading up to a collision. Based on the collected data, our detection approaches were able to detect targets at distances ranging from 400m to about 900m. These distances (with some assumptions about closing speeds and aircraft trajectories) translate to an advanced warning of between 8-10 seconds ahead of impact, which approaches the 12.5 second response time recommended for human pilots. We make use of the enormous potential of graphic processing units to achieve processing rates of 30Hz (for images of size 1024-by- 768). Currently, integration in the final platform is under way

Topics: 080100 ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING, 090100 AEROSPACE ENGINEERING, UAV sense and avoid, Hidden Markov Models, Computer vision
Publisher: IEEE
Year: 2010
DOI identifier: 10.1109/iros.2010.5651028
OAI identifier: oai:eprints.qut.edu.au:32793

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