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. this http URL.
Abstract (translated)
鲁棒的视觉同步定位与地图构建（vSLAM）对于在真实世界环境中运行的自主系统至关重要。动态物体、低纹理以及最关键的是光照条件的变化，常常会降低其性能。现有的基于特征的SLAM系统依赖于固定的前端参数设置，这使得它们对突然的照明变化和不稳定的特征跟踪变得脆弱。 为了解决这些问题，我们提出了“IRAF-SLAM”，这是一种设计用于提升vSLAM在复杂且挑战性环境中稳健性的光照鲁棒性和自适应特征剔除前端。我们的方法包括： 1. 一种图像增强方案，用于预处理并根据不同的光照条件调整图像质量； 2. 自适应的特征提取机制，该机制可以根据图像熵、像素强度和梯度分析动态地调整检测敏感性； 3. 基于密度分布分析和照明影响因子过滤不可靠特征点的特征剔除策略。 我们在TUM-VI和欧洲机器人挑战赛（EuRoC）数据集上进行了全面评估，结果表明IRAF-SLAM在不良光照条件下显著减少了跟踪失败，并且其轨迹准确性优于最先进的vSLAM方法。这些结果突显了自适应前端策略在不增加显著计算开销的情况下提升vSLAM鲁棒性方面的有效性。 IRAF-SLAM的实现代码公开可用，网址为https://thanhnguyencanh.github.io/iraf-slam/。
URL
https://arxiv.org/abs/2507.07752
