Vision-based Runway Detection and Landing for Unmanned Aerial Vehicle Enhanced Autonomy

Introducing autonomy is a task of paramount importance and is currently investigated in many areas, especially for autonomous cars and Unmanned Aerial Vehicles (UAVs). Most UAVs are still remotely human-controlled. A necessity is to implement on-board solutions, able to work in all weather condition...

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Bibliographic Details
Published in:	2023 IEEE International Conference on Mechatronics and Automation (ICMA) pp. 239 - 246
Main Authors:	Tsapparellas, Kyriacos, Jelev, Nickolay, Waters, Jonathon, Brunswicker, Sabine, Mihaylova, Lyudmila S
Format:	Conference Proceeding
Language:	English
Published:	IEEE 06-08-2023
Subjects:	Autonomous aerial vehicles Autonomous Landing Autonomy Computer vision Object tracking Real-time systems Runway Detection Statistical analysis Training Transfer learning Unmanned Aerial Vehicles (UAVs) Vision-based landing X-Plane 11 flight simulator
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Summary:	Introducing autonomy is a task of paramount importance and is currently investigated in many areas, especially for autonomous cars and Unmanned Aerial Vehicles (UAVs). Most UAVs are still remotely human-controlled. A necessity is to implement on-board solutions, able to work in all weather conditions and at any time. Hence, on this topic, we give an overview of recent advances for vision-based landing of UAVs. A thorough classification of the main recently developed methods is introduced with a discussion of their advantages and disadvantages. The paper presents a new solution for autonomous UAV vision-based landing, focusing on runway detection using a hybrid approach combining multi-image matching, SIFT and object tracking. The results are evaluated and validated using simulated images sampled with the X-Plane 11 flight simulator and real-world videos collected during automated flights performed by the ULTRA vehicle, one of the biggest UAVs in the UK [1]. The statistical analysis from the validation of the proposed approach shows a high level of accuracy around 94.89% in clear weather conditions and real-time computational performance.
ISSN:	2152-744X
DOI:	10.1109/ICMA57826.2023.10215523