发表机构
Applied Sciences Faculty, Ukrainian Catholic University（乌克兰天主教大学应用科学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究低光照环境下RGB相机的SLAM问题，对六种不同范式的系统在五个序列上进行基准测试，发现仅RGB的SLAM需惯性融合和全局优化才能稳定跟踪，为后续改进提供方向。
AI 中文摘要
同步定位与地图构建（SLAM）是机器人领域的基本问题之一，能实现现实场景中的自主操作。在低光照下，对比度降低、传感器噪声和运动模糊会影响特征提取与匹配，而使用激光雷达、深度或热传感器会增加成本等。现有基准测试多为光照良好的室内或白天序列。我们在五个不同难度和光照的LaMARia序列上对六种SLAM系统进行基准测试，报告绝对和相对位姿误差等。结果表明，仅RGB的SLAM只有在同时具备惯性融合和全局优化时才能在低光照下保持稳定跟踪。
英文摘要
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.
CommentsConducted as part of the certification program "Off-Road Visual Navigation: Development and Evaluation of Systems in Challenging Environments'' at the Faculty of Applied Sciences, Ukrainian Catholic University, in collaboration with the UCU UGV Club
