SLAM in Low-Light Environments: Project Report

Analysis

Why This Paper Matters

Simultaneous localization and mapping (SLAM) is a cornerstone of autonomous robotics, enabling navigation without prior maps. However, real-world environments often include low-light conditions—nighttime, tunnels, or indoor spaces with poor illumination—where standard RGB cameras suffer from reduced contrast, sensor noise, and motion blur. This paper directly addresses a critical gap: existing benchmarks are dominated by well-lit scenes, leaving practitioners uncertain about which SLAM systems can operate in darkness. By systematically evaluating six diverse SLAM paradigms on five challenging low-light sequences, the authors provide the first comprehensive comparison under realistic illumination stress. The findings are sobering: most classical and even learning-based systems fail or produce large errors, highlighting that low-light SLAM remains an open problem. This matters because autonomous systems—from warehouse robots to self-driving cars—must function reliably in all lighting conditions, and this paper sets a clear baseline for future improvements.

Technical Contributions

• Comprehensive benchmark design: The authors select six SLAM systems spanning feature-based (ORB-SLAM3), direct (DSO), filter-based (OpenVINS), and learning-based (DPVO, DPV-SLAM) paradigms, plus the hybrid Kimera-VIO. They use five LaMARia sequences with graded difficulty and illumination levels, ensuring diverse evaluation.
• Metric selection: They report absolute pose error (APE), relative pose error (RPE), and control-point recall, providing both global and local accuracy measures.
• Key insight on failure modes: The paper identifies that classical monocular pipelines (ORB-SLAM3, DSO) and filter-based OpenVINS fail outright or diverge on most harder low-light sequences, while DPVO and DPV-SLAM maintain tracking but with ~100 m absolute errors.
• Role of inertial fusion: Kimera-VIO, which fuses IMU data with visual measurements, is the only system to track all sequences, achieving the lowest RPE. However, its absolute error grows over time due to lack of loop closure, indicating that both inertial fusion and global optimization are necessary.
• Implication for future work: The results suggest that RGB-only SLAM is insufficient for low light; learned front-ends designed for low-light feature extraction or complementary sensors (e.g., LiDAR, thermal) may be required.

Results

Kimera-VIO achieves the best overall performance, tracking all five sequences with the lowest relative pose error (RPE). However, its absolute pose error (APE) grows steadily because it does not perform loop closure. DPVO and DPV-SLAM, both learning-based, never lose tracking but incur APE of roughly 100 meters under low light, indicating poor global consistency. ORB-SLAM3, DSO, and OpenVINS fail or diverge on most of the harder and low-light sequences, demonstrating that classical feature-based and direct methods are brittle in darkness. Control-point recall metrics further confirm that feature extraction and matching degrade significantly under low illumination. The paper does not provide exact numerical values for all metrics, but the qualitative trends are clear: only Kimera-VIO provides reliable tracking, and even it has limitations.

Significance

This paper has immediate practical implications for robotics practitioners deploying SLAM in challenging environments. It provides a clear ranking of existing systems under low light, saving teams from costly trial-and-error. The finding that inertial fusion (IMU) is critical for stability suggests that future SLAM systems should integrate visual-inertial odometry as a baseline. The failure of learning-based methods like DPVO to achieve low absolute error indicates that current learned front-ends are not robust to low-light degradation, opening a research direction for low-light-specific feature learning or end-to-end training on dark sequences. The paper also underscores the need for global optimization (loop closure) to prevent drift, even when tracking is stable. For the broader AI field, this work highlights that domain-specific benchmarks (e.g., low-light) are essential to uncover weaknesses in general-purpose models, and that sensor fusion remains a practical necessity until learned methods can match the robustness of classical approaches in extreme conditions.