What question did this study set out to answer?

This research aims to improve the robustness of visual SLAM in dim-light environments by introducing new image preprocessing techniques.

April 1, 2026Open Access

Dim-Light Robust Monocular SLAM : How Image Processing Technology Boosts the Robustness of Traditional Visual SLAM in Dim-Light Environment

Key Points

This research aims to improve the robustness of visual SLAM in dim-light environments by introducing new image preprocessing techniques.
Developed DRMS (Dim-light Robust Monocular SLAM) method combining image preprocessing with Monocular SLAM.
Applied linear transformation and CLAHE to enhance image brightness and contrast.
Utilized optical flow algorithm to track features and reduce computational complexity.
Validated method on public datasets and through real-world experiments.
Achieved higher accuracy in feature point detection in dim-light conditions compared to existing visual SLAM methods.
Demonstrated increased reliability of SLAM performance under low light illumination.
Showed significant improvement in operational efficiency with reduced computation time.

Abstract

SLAM is the abbreviation of simultaneous localization and mapping. The mainstream methods of SLAM are Lidar SLAM and Visual SLAM. Compared with Lidar SLAM, Visual SLAM is cheaper, content distinguishable and easy to get. However, Lighting conditions are critical to the performance of visual SLAM system. Especially, challenge still remains for adopting visual SLAM in dim-light environment since it’s difficult to detect enough valid feature points. To address this issue, we propose DRMS (Dim-light Robust Monocular SLAM), a new method combining image preprocessing, which includes linear transformation and CLAHE, with the Monocular SLAM system. After applying the linear transformation and CLAHE, the brightness and contrast of the images would be significantly increased, and adequate feature points would be detected. Moreover, we use optical flow algorithm to track the features in order to reduce computation complexity. The performance of our method is validated both on public dataset and real-world experiment. The results show that our proposal is more reliable and of higher accuracy in dim-light conditions than other existing work.

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