SLAM in Low-Light Environments: Project Report

2026-07-20

AI Summary

This study systematically evaluates six representative SLAM approaches—ORB-SLAM3, DSO, Kimera-VIO, OpenVINS, DPVO, and DPV-SLAM—under low-light conditions using a unified benchmark, the LaMARia dataset. Encompassing feature-based, direct, filtering, and learning-based paradigms, the methods are quantitatively assessed via absolute and relative pose errors as well as control point recall rates to analyze accuracy and robustness. Results reveal that only Kimera-VIO successfully tracks all sequences with the lowest relative error, while DPVO and DPV-SLAM, though frame-loss-free, exhibit absolute trajectory errors on the order of hundreds of meters. Other RGB-only methods generally fail. The findings underscore the critical role of inertial fusion and global optimization in enhancing robustness under poor illumination and delineate the current performance limits of purely visual SLAM in low-light scenarios.

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.

Problem

• SLAM
• low-light
• RGB-only
• feature extraction
• pose estimation

Innovation

• low-light SLAM
• RGB-only
• inertial fusion
• benchmarking
• visual-inertial odometry