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

• 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
• ISSN: 2152-744X
• DOI: 10.1109/ICMA57826.2023.10215523

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.