IRAF-SLAM: 厳しい環境下での視覚SLAMのための照明に頑健で適応的な特徴選別フロントエンド
IRAF-SLAM: An Illumination-Robust and Adaptive Feature-Culling Front-End for Visual SLAM in Challenging Environments
照明変化に強い画像強調と、画像エントロピー・輝度・勾配に基づく適応的な特徴抽出、信頼性の低い特徴点を除去する選別戦略を組み合わせた視覚SLAMのフロントエンドを提案し、TUM-VIとEuRoCで追跡失敗の減少と軌道精度の向上を示した。
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著者: Thanh Nguyen Canh, Bao Nguyen Quoc, Haolan Zhang, Bupesh Rethinam Veeraiah, Xiem HoangVan, Nak Young Chong
分類: cs.RO
原文アブストラクト
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/.
