Improving the performance of the ORB-SLAM3 with low-light image enhancement
Bing Han, Tuan Li, Zhixin Wang, Chuang Shi · 2024
Traditional Visual Simultaneous Localization and Mapping (VSLAM) algorithms demonstrate good accuracy and robustness in well-lighting environments by extracting numerous feature points. However, in low-light conditions, insufficient illumination leads to low-contrast images, which hampers the ability of the front-end to extract adequate feature points, resulting in increased tracking errors or complete tracking failure. To overcome these limitations, this paper improves the performance of the ORB-SLAM3 in low-light environments with image enhancement capability. Specifically, we integrate the Histogram Equalization Prior-based (HEP) image enhancement module into ORB-SLAM3. Furthermore, we compare and analyze the results with two other image enhancement algorithms to ensure the effective fusion of image enhancement and ORB-SLAM3. Experiments were conducted and performance comparisons were made to validate the performance of ORB-SLAM3 with image enhancement in low-light environments. Experimental results indicate that the Root Mean Square Errors (RMSE) of the Absolute Pose Error (APE) are 0.99 cm and 0.76 cm on the two public ETH3D low-light datasets, respectively. Compared to the original ORB-SLAM3, the improvement is over 50%. Similarly, the RMSE of the Relative Pose Error (RPE) are 0.54 cm and 0.65 cm, with an improvement of 29% and 34%, respectively.