Wood, H., Mills, A. orcid.org/0000-0002-6798-5284, Jacobs, W. et al. (1 more author) (2025) Geometrically constrained position estimation through low-level tracking. In: Proceedings of 2025 19th International Conference on Machine Vision Applications (MVA). 2025 19th International Conference on Machine Vision Applications (MVA), 26-28 Jul 2025, Kyoto, Japan. Institute of Electrical and Electronics Engineers (IEEE).
Abstract
Driven by the aerospace need to better monitor landing gear structures, we propose a vision-based position estimation framework to determine the configuration of a series of articulated rigid links in a harsh environment through leveraging knowledge of the subject geometry. The framework is capable of running on compute-limited hardware by exploiting the inherent efficiency of low-level point trackers. The framework is designed to simultaneously overcome localised image occlusions and scene vibration by combining a Kalman filter with a geometry-aware mixture model to realign tracking points. We demonstrate the effectiveness of compensating for low-cost tracker drift by presenting the position estimation performance under nominal and disturbance conditions and compare this approach to dedicated object-specific methods showcasing superior resilience to typical disturbances and high estimation speed.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2025 The Author(s). |
Dates: |
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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: | 19 Sep 2025 11:28 |
Last Modified: | 19 Sep 2025 11:28 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Refereed: | Yes |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:231844 |
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Filename: Geometrically_Constrained_Position_Estimation_through_Low_level_Tracking (1).pdf
