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J Intell Robot Syst (2016) 84:121144 DOI 10.1007/s10846-015-0285-0
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Web End = Sky Region Obstacle Detection and Tracking for Vision-Based UAS Sense and Avoid
Giancarmine Fasano Domenico Accardo
Anna Elena Tirri Antonio Moccia
Ettore De Lellis
Received: 22 December 2014 / Accepted: 28 September 2015 / Published online: 20 October 2015 Springer Science+Business Media Dordrecht 2015
Abstract A customized detection and tracking algorithm for vision-based non cooperative UAS sense and avoid aimed at obstacles approaching from above the horizon is presented in this paper. The proposed approach comprises two main steps. Specifically, the first processing step is relevant to obstacle detection and tentative tracking for track confirmation and is based on top-hat and bottom-hat morphological filtering, local image analysis for a limited set of regions of interest, and multi-frame processing in stabilized coordinates. Once firm tracking is achieved, template matching and state estimation based on Kalman filtering are used to track the intruder aircraft and estimate its angular position and velocity. An extensive experimental analysis is presented which is based on a large set of flight data gathered in realistic near collision scenarios, in different operating conditions in terms of weather and illumination, and adopting different navigation units onboard the ownship. In particular, the focus is set on flight segments at a range between 3 km
G. Fasano ([envelopeback]) D. Accardo A. E. Tirri A. Moccia
Department of Industrial Engineering - Aerospace
Engineering Division, University of Naples Federico II,P.le Tecchio 80, 80125 Napoli, Italy e-mail: mailto:[email protected]
Web End [email protected]
E. De LellisOn-Board Systems And ATM Information Technologies Department, Italian Aerospace Research Center (CIRA), Via Maiorise, 81043 Capua, CE, Italy
and 1.3 km, since the major interest is in understanding algorithm potential for relatively large time to collision. System performance is evaluated in terms of declaration range, probability of correct declaration, average number of false positives, tracking accuracy (angles and angular rates in a stabilized North-East-Down reference frame) and robustness with respect to track loss phenomena. Promising results are achieved regarding the trade-off between declaration range and false alarm probability, while the onboard navigation unit is found to heavily impact tracking accuracy.
Keywords Unmanned aircraft systems
Non-cooperative sense and avoid Vision-based
detection and tracking Flight tests
1 Introduction
Enabling the routine access...