Fusion of data and knowledge for safe UAV landing

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McClean, Sally, Scotney, Bryan, Patterson, Timothy, Morrow, Philip and Parr, Gerard ORCID: https://orcid.org/0000-0002-9365-9132 (2012) Fusion of data and knowledge for safe UAV landing. International Journal of Software and Informatics, 6 (3). pp. 381-398. ISSN 1673-7288

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Abstract

Autonomous Unmanned Aerial Vehicles (UAVs) have the potential to significantly improve current working practices for a variety of applications including aerial surveillance and search-and-rescue. However before UAVs can be widely integrated into civilian airspace there are a number of technical challenges which must be overcome including provision of an autonomous method of landing which would be executed in the event of an emergency. A fundamental component of autonomous landing is safe landing zone detection of which terrain classification is a major constituent. Presented in this paper is an extension of the Multi-Modal Expectation Maximization algorithm which combines data in the form of multiple images of the same scene, with knowledge in the form of historic training data and Ordnance Survey map information to compute updated class parameters. These updated parameters are subsequently used to classify the terrain of an area based on the pixel data contained within the images. An image's contribution to the classification of an area is then apportioned according to its coverage of that area. Preliminary results are presented based on aerial imagery of the Antrim Plateau region in Northern Ireland which indicates potential in the approach used.

Item Type: Article
Uncontrolled Keywords: multi-resolution expectation maximization algorithm ,uav safe landing zone detection,uav terrain classification
Faculty \ School: Faculty of Science > School of Computing Sciences
UEA Research Groups: Faculty of Science > Research Groups > Smart Emerging Technologies
Faculty of Science > Research Groups > Cyber Security Privacy and Trust Laboratory
Faculty of Science > Research Groups > Data Science and AI
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Depositing User: Pure Connector
Date Deposited: 15 Nov 2016 16:00
Last Modified: 10 Dec 2024 01:28
URI: https://ueaeprints.uea.ac.uk/id/eprint/61356
DOI:

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