Haggart, R. and Aitken, J.M. orcid.org/0000-0003-4204-4020 (2021) Online scene visibility estimation as a complement to SLAM in UAVs. In: Fox, C., Gao, J., Esfahani, A.G., Saaj, M., Hanheide, M. and Parsons, S., (eds.) Towards Autonomous Robotic Systems : 22nd Annual Conference, TAROS 2021, Lincoln, UK, September 8–10, 2021, Proceedings. TAROS 2021 : Towards Autonomous Robotic Systems, 08-10 Sep 2021, Lincoln, UK. Lecture Notes in Computer Science (13054). Springer Cham , pp. 365-369. ISBN 9783030891763
Abstract
Simultaneous localisation and mapping (SLAM) relies on low-cost on-board sensors such as cameras and inertial measurement units. It is crucial that the surroundings are visible to the cameras to maximise the accuracy of the system. An estimation strategy is proposed to augment ORB-SLAM2 that considers feature extraction capability, distribution of the extracted features in the image frame, and the ability of the algorithm to track features over time. The method is tested on challenging datasets, and the output is evaluated against different visibility conditions. The proposed method is shown to react appropriately and consistently to ‘less visible’ conditions such as fog, sunlight, and rapid motion in real time, with minimal computational load.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2021 Springer Nature Switzerland AG. This is an author-produced version of a paper subsequently published in TAROS 2021 Proceedings. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Simultaneous Localisation and Mapping; Visibility |
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 Engineering and Physical Sciences Research Council EP/S016813/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 18 May 2022 09:16 |
Last Modified: | 31 Oct 2022 01:13 |
Status: | Published |
Publisher: | Springer Cham |
Series Name: | Lecture Notes in Computer Science |
Refereed: | Yes |
Identification Number: | 10.1007/978-3-030-89177-0_38 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:187005 |