Tsapparellas, K., Jelev, N., Waters, J. et al. (2 more authors) (2023) Vision-based runway detection and landing for unmanned aerial vehicle enhanced autonomy. In: 2023 IEEE International Conference on Mechatronics and Automation (ICMA) Proceedings. 2023 IEEE International Conference on Mechatronics and Automation (ICMA), 06-09 Aug 2023, Harbin, Heilongjiang, China. Institute of Electrical and Electronics Engineers (IEEE) , pp. 239-246. ISBN 9798350320855
Abstract
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.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 The Authors. Except as otherwise noted, this author-accepted version of a paper published in 2023 IEEE International Conference on Mechatronics and Automation (ICMA) Proceedings is made available via the University of Sheffield Research Publications and Copyright Policy under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ |
Keywords: | Unmanned Aerial Vehicles (UAVs); Autonomous Landing; Runway Detection; Autonomy; X-Plane 11 flight simulator; Computer Vision; Vision-based landing |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Funding Information: | Funder Grant number INNOVATE UK 10023377 TS/W02005X/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 14 Jun 2023 14:04 |
Last Modified: | 05 Mar 2025 16:51 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Refereed: | Yes |
Identification Number: | 10.1109/ICMA57826.2023.10215523 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:200327 |