On motion blur and deblurring in visual place recognition
On motion blur and deblurring in visual place recognition
Visual Place Recognition (VPR) in mobile robotics enables robots to localize themselves by recognizing previously visited locations using visual data. While the reliability of VPR methods has been extensively studied under conditions such as changes in illumination, season, weather and viewpoint, the impact of motion blur is relatively unexplored despite its relevance not only in rapid motion scenarios but also in low-light conditions where longer exposure times are necessary. Similarly, the role of image deblurring in enhancing VPR performance under motion blur has received limited attention so far. This letter bridges these gaps by introducing a new benchmark designed to evaluate VPR performance under the influence of motion blur and image deblurring. The benchmark includes three datasets that encompass a wide range of motion blur intensities, providing a comprehensive platform for analysis. Experimental results with several well-established VPR and image deblurring methods provide new insights into the effects of motion blur and the potential improvements achieved through deblurring. Building on these findings, the letter proposes adaptive deblurring strategies for VPR, designed to effectively manage motion blur in dynamic, real-world scenarios.
data sets for robotic vision, Localization, vision-based navigation
4746-4753
Ismagilov, Timur
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Ferrarini, Bruno
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Milford, Michael
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Tan Viet Tuyen, Nguyen
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Ramchurn, Sarvapali D.
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Ehsan, Shoaib
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26 March 2025
Ismagilov, Timur
0654f7ae-b29d-4708-bf5f-b834d69a00a5
Ferrarini, Bruno
a93ab204-5ccf-4b6d-a7c2-e02e65729924
Milford, Michael
9edf5ef3-4a6a-4d05-aec2-6146c00cd407
Tan Viet Tuyen, Nguyen
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Ramchurn, Sarvapali D.
1d62ae2a-a498-444e-912d-a6082d3aaea3
Ehsan, Shoaib
ae8922f0-dbe0-4b22-8474-98e84d852de7
Ismagilov, Timur, Ferrarini, Bruno, Milford, Michael, Tan Viet Tuyen, Nguyen, Ramchurn, Sarvapali D. and Ehsan, Shoaib
(2025)
On motion blur and deblurring in visual place recognition.
IEEE Robotics and Automation Letters, 10 (5), .
(doi:10.1109/LRA.2025.3554103).
Abstract
Visual Place Recognition (VPR) in mobile robotics enables robots to localize themselves by recognizing previously visited locations using visual data. While the reliability of VPR methods has been extensively studied under conditions such as changes in illumination, season, weather and viewpoint, the impact of motion blur is relatively unexplored despite its relevance not only in rapid motion scenarios but also in low-light conditions where longer exposure times are necessary. Similarly, the role of image deblurring in enhancing VPR performance under motion blur has received limited attention so far. This letter bridges these gaps by introducing a new benchmark designed to evaluate VPR performance under the influence of motion blur and image deblurring. The benchmark includes three datasets that encompass a wide range of motion blur intensities, providing a comprehensive platform for analysis. Experimental results with several well-established VPR and image deblurring methods provide new insights into the effects of motion blur and the potential improvements achieved through deblurring. Building on these findings, the letter proposes adaptive deblurring strategies for VPR, designed to effectively manage motion blur in dynamic, real-world scenarios.
Text
2412.07751v1
- Accepted Manuscript
More information
Accepted/In Press date: 13 March 2025
Published date: 26 March 2025
Keywords:
data sets for robotic vision, Localization, vision-based navigation
Identifiers
Local EPrints ID: 507348
URI: http://eprints.soton.ac.uk/id/eprint/507348
ISSN: 2377-3766
PURE UUID: 20b60a6c-2dae-4f62-b560-1b24ea3fa2f3
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Date deposited: 04 Dec 2025 18:00
Last modified: 05 Dec 2025 03:02
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Contributors
Author:
Timur Ismagilov
Author:
Bruno Ferrarini
Author:
Michael Milford
Author:
Nguyen Tan Viet Tuyen
Author:
Sarvapali D. Ramchurn
Author:
Shoaib Ehsan
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