Detection of Aircraft Below The Horizon for Vision-Based Detect And Avoid in Unmanned Aircraft Systems

Molloy, Timothy L., Ford, Jason J., & Mejias, Luis (2017) Detection of Aircraft Below The Horizon for Vision-Based Detect And Avoid in Unmanned Aircraft Systems. Journal of Field Robotics. (In Press)

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Abstract

Vision-based aircraft detection technology may provide a credible sensing option for automated detect and avoid in small to medium size fixed-wing unmanned aircraft systems (UAS). Reliable vision-based aircraft detection has previously been demonstrated in sky-region sensing environments. This paper describes a novel vision-based system for detecting aircraft below the horizon in the presence of ground clutter. We examine the performance of our system on a data set of 63 near collision encounters we collected between a camera- equipped manned aircraft, and a below-horizon target. In these 63 encounters, our system successfully detects all aircraft, at an average detection range of 1890m (with a standard error of 43m and no false alarms in 1.1 hours). Furthermore, our system does not require access to inertial sensor data (which significantly reduces system cost), and operates at over 12 frames per second.

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ID Code: 105590
Item Type: Journal Article
Refereed: Yes
Keywords: Sense and avoid, Machine vision, Hidden Markov models, Object detection, Unmanned aerial vehicles (UAV), Unmanned aircraft systems (UAS), See and avoid
DOI: 10.1002/rob.21719
ISSN: 1556-4967
Subjects: Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600) > Control Systems Robotics and Automation (090602)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: John Wiley & Sons
Deposited On: 10 Apr 2017 22:43
Last Modified: 11 Apr 2017 23:15

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