Airborne vision-based collision-detection system

Lai, John S., Mejias, Luis, & Ford, Jason J. (2011) Airborne vision-based collision-detection system. Journal of Field Robotics, 28(2), pp. 137-157.

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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 vision sensors (as opposed to radar and TCAS). This paper describes the development and evaluation of a real-time vision-based collision detection system suitable for fixed-wing aerial robotics. Using two fixed-wing UAVs to recreate various collision-course scenarios, we were able to capture highly realistic vision (from an onboard camera perspective) of the moments leading up to a collision. This type of image data is extremely scarce and was invaluable in evaluating the detection performance of two candidate target detection approaches. 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 overcame the challenge of achieving real-time computational speeds by exploiting the parallel processing architectures of graphics processing units found on commercially-off-the-shelf graphics devices. Our chosen GPU device suitable for integration onto UAV platforms can be expected to handle real-time processing of 1024 by 768 pixel image frames at a rate of approximately 30Hz. Flight trials using manned Cessna aircraft where all processing is performed onboard will be conducted in the near future, followed by further experiments with fully autonomous UAV platforms.

Impact and interest:

66 citations in Scopus
38 citations in Web of Science®
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ID Code: 32801
Item Type: Journal Article
Refereed: Yes
Keywords: collision avoidance, see-and-avoid, sense-and-avoid, machine vision, unmanned aerial vehicle
DOI: 10.1002/rob.20359
ISSN: 1556-4959
Subjects: Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > AEROSPACE ENGINEERING (090100)
Divisions: Current > Research Centres > Australian Research Centre for Aerospace Automation
Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Copyright Owner: Copyright 2010 Wiley-Blackwell Publishing
Copyright Statement: The definitive version is available at
Deposited On: 21 Jun 2010 21:43
Last Modified: 03 Jul 2017 14:43

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