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

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.