ICUAS'17 Paper Abstract


Paper ThC2.1

Bauer, Peter (Institute for Computer Science and Control, HungarianAcademyof S), Hiba, Antal (Hungarian Academy of Sciences Institute for Computer Science and), Bokor, Jozsef (Hungarian Academy of Sciences)

Monocular Image-Based Intruder Direction Estimation at Closest Point of Approach

Scheduled for presentation during the "See-and-avoid Systems - II" (ThC2), Thursday, June 15, 2017, 15:40−16:00, Salon AB

2017 International Conference on Unmanned Aircraft Systems, June 13-16, 2017, Miami Marriott Biscayne Bay, Miami, FL,

This information is tentative and subject to change. Compiled on April 12, 2021

Keywords See-and-avoid Systems, Smart Sensors, UAS Applications


This paper deals with monocular image-based aircraft Sense and Avoid for small UAVs. After summarizing previous results of the authors it proposes a complete solution which calculates time to closest point of approach, relative closest point of approach (CPA) and the direction of intruder at CPA. These parameters are enough to make a collision decision and design the avoidance maneuver. The applicability of the proposed solution is demonstrated considering an omnidirectional multi-camera system in an extensive softwarein-the-loop test campaign covering the whole possible size and velocity range of manned aircraft as intruder. Straight aircraft paths with constant velocity and camera pixelization errors were considered. Almost 100% decision success was achieved. After the simulations the solution is demonstrated on real flight test data and even in real flight giving 90% avoidance success (10% missed detection) in close and 60% decision success (40% false alarm) in far encounters.



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