低光照環境下的SLAM：項目報告

摘要

同時定位與地圖構建（SLAM）是機器人學中的基本問題之一，因其能使自主系統在現實場景中運作。在低照度條件下，對比度降低、感測器雜訊及運動模糊會同時削弱特徵提取與特徵匹配的表現；而透過光達（LiDAR）、深度感測器或熱感測器進行補償，則會增加成本、功耗與系統整合複雜度。現有基準測試多以照明充足的室內或白天序列為主，因此標準RGB相機SLAM在黑暗中能達到何種程度仍是未解之題。我們針對五組難度與照明條件各異的LaMARia序列，對六種涵蓋基於特徵、直接法、濾波器式及學習型典範的系統——ORB-SLAM3、DSO、Kimera-VIO、OpenVINS、DPVO與DPV-SLAM——進行基準測試，並報告其絕對與相對姿態誤差以及控制點召回率。Kimera-VIO是唯一能完整追蹤所有五組序列的系統，它在達到最低相對姿態誤差的同時，因缺乏閉環檢測而導致絕對誤差持續增長；DPVO與DPV-SLAM雖從未失去追蹤，但在低光照下絕對誤差約達100公尺；而經典的單眼視覺管線（ORB-SLAM3、DSO）與濾波器式OpenVINS則在多數較困難與低光照序列中直接失效或發散。結果顯示，僅使用RGB相機的SLAM唯有在同時具備慣性融合與全局優化時，才得以維持穩定的低光照追蹤。若要彌補其餘差距，可能需要開發專門應對低光照的學習型前端，或回歸互補式感測方案。

English

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.