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Please use this identifier to cite or link to this item:
https://hdl.handle.net/10119/19987
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| Title: | IRAF-SLAM: An Illumination-Robust and Adaptive Feature-Culling Front-End for Visual SLAM in Challenging Environments |
| Authors: | Nguyen Canh, Thanh
Nguyen Quoc, Bao
Zhang, Haolan
Veeraiah, Bupesh Rethinam
HoangVan, Xiem
Chong, Nak Young |
| Keywords: | Robust Front-End
Illumination Adaptation
Feature Culling
Visual SLAM |
| Issue Date: | 2025-09-18 |
| Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
| Magazine name: | 2025 European Conference on Mobile Robots (ECMR) |
| Start page: | 1 |
| End page: | 7 |
| DOI: | 10.1109/ECMR65884.2025.11163050 |
| Abstract: | Robust Visual SLAM (vSLAM) is essential for autonomous systems operating in real-world environments, where challenges such as dynamic objects, low texture, and critically, varying illumination conditions often degrade performance. Existing feature-based SLAM systems rely on fixed front-end parameters, making them vulnerable to sudden lighting changes and unstable feature tracking. To address these challenges, we propose “IRAF-SLAM”, an Illumination-Robust and Adaptive Feature-Culling front-end designed to enhance vSLAM resilience in complex and challenging environments. Our approach introduces: (1) an image enhancement scheme to preprocess and adjust image quality under varying lighting conditions; (2) an adaptive feature extraction mechanism that dynamically adjusts detection sensitivity based on image entropy, pixel intensity, and gradient analysis; and (3) a feature culling strategy that filters out unreliable feature points using density distribution analysis and a lighting impact factor. Comprehensive evaluations on the TUM-VI and European Robotics Challenge (EuRoC) datasets demonstrate that IRAF-SLAM significantly reduces tracking failures and achieves superior trajectory accuracy compared to state-of-the-art vSLAM methods under adverse illumination conditions. These results highlight the effectiveness of adaptive front-end strategies in improving vSLAM robustness without incurring significant computational overhead. The implementation of IRAF-SLAM is publicly available at https://thanhnguyencanh.github.io/IRAF-SLAM/. |
| Rights: | This is the author's version of the work. Copyright (C) 2025 IEEE. 2025 European Conference on Mobile Robots (ECMR), Padova, Italy, pp. 1-7. DOI: https://doi.org/10.1109/ECMR65884.2025.11163050. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| URI: | https://hdl.handle.net/10119/19987 |
| Material Type: | author |
| Appears in Collections: | b11-1. 会議発表論文・発表資料 (Conference Papers)
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| N-CHONG-I-0919.pdf | | 1704Kb | Adobe PDF | View/Open |
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