Low-light Image Enhancement for Construction Robot Simultaneous Localization and Mapping

Visual Simultaneous Localization and Mapping (V-SLAM) is commonly employed as a method in construction robots due to its efficient and cost-effective nature for information acquisition. However, detection and positioning using V-SLAM face significant challenges in low-light construction scenes, such as underground garages or dim indoor locations, where detecting enough valid feature points can be difficult leading to navigation failure. In this study, we propose an Unsupervised V-SLAM Light Enhancement Network (UVLE-Net) to address this issue by enhancing the quality of low-light images. Subsequently, we incorporated the robust Shi-Tomasi method in ORB-SLAM2 to detect feature points and utilized the sparse optical flow algorithm to track them. The use of UVLE-Net significantly increased the brightness and contrast of the images, allowing for easy detection of feature points. Furthermore, the Shi-Tomasi method and sparse optical flow algorithm were found to be effective in improving the ability of feature point extraction and tracking in low-light conditions. To validate the robustness and superiority of our approach, we carried out comparison experiments with other enhancement techniques, using publicly available and real-world construction datasets.