LumiLoc: A Low-Light-Optimized Visual Localization Framework for Autonomous Drones
In low-light conditions, UAV localization faces substantial challenges due to reduced visibility, elevated noise levels, and diminished contrast. To address these issues, we propose a low-light-optimized visual localization framework that integrates an attention-based image enhancement module, a rob...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/12/6/454 |
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| author | Ruokun Qu Zhiyuan Wang Yelu Liu Chenglong Li Hui Jiang Chen Fang |
| author_facet | Ruokun Qu Zhiyuan Wang Yelu Liu Chenglong Li Hui Jiang Chen Fang |
| author_sort | Ruokun Qu |
| collection | DOAJ |
| description | In low-light conditions, UAV localization faces substantial challenges due to reduced visibility, elevated noise levels, and diminished contrast. To address these issues, we propose a low-light-optimized visual localization framework that integrates an attention-based image enhancement module, a robust feature extraction network tailored for degraded environments, and a lightweight pose estimation algorithm that fuses geometric and convolutional features. Extensive evaluations on both real-world and synthetic low-light datasets reveal significant improvements in accuracy, noise resilience, and adaptability to dynamic lighting. Moreover, experimental results validate the framework’s feasibility for applications in night operations, urban air traffic management, and disaster response, thereby effectively overcoming the critical limitations of UAV positioning under low-light conditions. |
| format | Article |
| id | doaj-art-ac5e3a39a0ee499f9db5bae07f5e369b |
| institution | OA Journals |
| issn | 2226-4310 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj-art-ac5e3a39a0ee499f9db5bae07f5e369b2025-08-20T02:24:01ZengMDPI AGAerospace2226-43102025-05-0112645410.3390/aerospace12060454LumiLoc: A Low-Light-Optimized Visual Localization Framework for Autonomous DronesRuokun Qu0Zhiyuan Wang1Yelu Liu2Chenglong Li3Hui Jiang4Chen Fang5College of Air Traffic Management, Civil Aviation Flight University of China, Chengdu 618307, ChinaCollege of Air Traffic Management, Civil Aviation Flight University of China, Chengdu 618307, ChinaCollege of Air Traffic Management, Civil Aviation Flight University of China, Chengdu 618307, ChinaCollege of Air Traffic Management, Civil Aviation Flight University of China, Chengdu 618307, ChinaSchool of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610050, ChinaCollege of Air Traffic Management, Civil Aviation Flight University of China, Chengdu 618307, ChinaIn low-light conditions, UAV localization faces substantial challenges due to reduced visibility, elevated noise levels, and diminished contrast. To address these issues, we propose a low-light-optimized visual localization framework that integrates an attention-based image enhancement module, a robust feature extraction network tailored for degraded environments, and a lightweight pose estimation algorithm that fuses geometric and convolutional features. Extensive evaluations on both real-world and synthetic low-light datasets reveal significant improvements in accuracy, noise resilience, and adaptability to dynamic lighting. Moreover, experimental results validate the framework’s feasibility for applications in night operations, urban air traffic management, and disaster response, thereby effectively overcoming the critical limitations of UAV positioning under low-light conditions.https://www.mdpi.com/2226-4310/12/6/454low-light UAV localizationimage enhancementfeature extractionpose estimationnoise robustnessdynamic lighting adaptability |
| spellingShingle | Ruokun Qu Zhiyuan Wang Yelu Liu Chenglong Li Hui Jiang Chen Fang LumiLoc: A Low-Light-Optimized Visual Localization Framework for Autonomous Drones Aerospace low-light UAV localization image enhancement feature extraction pose estimation noise robustness dynamic lighting adaptability |
| title | LumiLoc: A Low-Light-Optimized Visual Localization Framework for Autonomous Drones |
| title_full | LumiLoc: A Low-Light-Optimized Visual Localization Framework for Autonomous Drones |
| title_fullStr | LumiLoc: A Low-Light-Optimized Visual Localization Framework for Autonomous Drones |
| title_full_unstemmed | LumiLoc: A Low-Light-Optimized Visual Localization Framework for Autonomous Drones |
| title_short | LumiLoc: A Low-Light-Optimized Visual Localization Framework for Autonomous Drones |
| title_sort | lumiloc a low light optimized visual localization framework for autonomous drones |
| topic | low-light UAV localization image enhancement feature extraction pose estimation noise robustness dynamic lighting adaptability |
| url | https://www.mdpi.com/2226-4310/12/6/454 |
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