外观
论文arXiv 2607.176993 个月前
低光照环境中的 SLAM：项目报告
原文链接：SLAM in Low-Light Environments: Project Report（arXiv 2607.17699 · 发布日期：2026-07-20）
SLAM in Low-Light Environments: Project Report
该报告针对低光照下RGB相机SLAM鲁棒性问题，基准测试六个系统在五个LaMARia序列上的表现。
核心要点
- 低照度造成对比度降低、传感器噪声和运动模糊，削弱特征提取与匹配。
- 方法是比较特征法、直接法、滤波法和学习法范式下的六个SLAM系统。
- 实验在五个LaMARia序列上报告绝对/相对位姿误差和控制点召回率。
- Kimera-VIO完成全部序列；DPVO和DPV-SLAM不丢跟踪但低光下绝对误差约100米。
验证范围与局限：验证范围限于五个不同难度和光照的LaMARia序列上的六个SLAM系统。
英文摘要 (Abstract)
Simultaneous localization and mapping (SLAM) is one of the fundamental problems in robotics, as it enables autonomous operations in real-world scenarios. Under low illumination, reduced contrast, sensor noise, and motion blur degrade both feature extraction and feature matching, while compensating with LiDAR, depth, or thermal sensors raises cost, power draw, and integration complexity. Existing benchmarks remain dominated by well-lit indoor or daylight sequences, leaving open how far SLAM with standard RGB cameras can be pushed in the dark. We benchmark six systems spanning the feature-based, direct, filter-based, and learning-based paradigms - ORB-SLAM3, DSO, Kimera-VIO, OpenVINS, DPVO, and DPV-SLAM - on five LaMARia sequences of varying difficulty and illumination, reporting absolute and relative pose error alongside control-point recall. Kimera-VIO is the only system to track all five sequences to completion, combining the lowest relative pose error with steadily growing absolute error due to the absence of loop closure; DPVO and DPV-SLAM never lose tracking but incur absolute errors of roughly 100 m under low light; and the classical monocular pipelines (ORB-SLAM3, DSO) together with the filter-based OpenVINS fail outright or diverge on most of the harder and low-light sequences. The results suggest that RGB-only SLAM maintains stable low-light tracking only when both inertial fusion and global optimization are present. Closing the remaining gap will likely require low-light-specific learned front-ends or a return to complementary sensing.
作者：Oleh Basystyi, Anna Stasyshyn, Oleksandr Kosovan, Yaroslav Prytula
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