Tsapparellas, K., Jelev, N., Waters, J. et al. (3 more authors) (2024) A versatile real-time vision-led runway localisation system for enhanced autonomy. Frontiers in Robotics and AI, 11. 1490812. ISSN 2296-9144
Abstract
This paper proposes a solution to the challenging task of autonomously landing Unmanned Aerial Vehicles (UAVs). An onboard computer vision module integrates the vision system with the ground control communication and video server connection. The vision platform performs feature extraction using the Speeded Up Robust Features (SURF), followed by fast Structured Forests edge detection and then smoothing with a Kalman filter for accurate runway sidelines prediction. A thorough evaluation is performed over real-world and simulation environments with respect to accuracy and processing time, in comparison with state-of-the-art edge detection approaches. The vision system is validated over videos with clear and difficult weather conditions, including with fog, varying lighting conditions and crosswind landing. The experiments are performed using data from the X-Plane 11 flight simulator and real flight data from the Uncrewed Low-cost TRAnsport (ULTRA) self-flying cargo UAV. The vision-led system can localise the runway sidelines with a Structured Forests approach with an accuracy approximately 84.4%, outperforming the state-of-the-art approaches and delivering real-time performance. The main contribution of this work consists of the developed vision-led system for runway detection to aid autonomous landing of UAVs using electro-optical cameras. Although implemented with the ULTRA UAV, the vision-led system is applicable to any other UAV.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2024 Tsapparellas, Jelev, Waters, Shrikhande, Brunswicker and Mihaylova. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
Keywords: | aerial systems: perception and autonomy; vision-based navigation; computer vision for automation; autonomous landing; autonomous vehicle navigation |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > School of Electrical and Electronic Engineering |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 10 Dec 2024 15:51 |
Last Modified: | 10 Dec 2024 15:51 |
Status: | Published |
Publisher: | Frontiers Media S.A. |
Refereed: | Yes |
Identification Number: | 10.3389/frobt.2024.1490812 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:220565 |