Tunnel Mapping in Low-Light Environments: A Synergistic Scheme of Image Enhancement and Multi-Source Factor Graph Optimization

Tunnel environments often suffer from GPS denial, uneven illumination, and structural uniformity, which lead to feature degradation, loop closure failure, and long-distance drift in SLAM systems. To solve these problems, this study aims to propose a high-precision SLAM method suitable for tunnel structural health monitoring. Firstly, an ABA-CLAHE image enhancement algorithm is proposed, which adopts cascaded processing of nonlinear brightness adjustment in HSV space and CLAHE local contrast optimization to improve low-light image quality and enhance feature stability. Then, SURF feature matching combined with the RANSAC algorithm is used to ensure feature matching accuracy. Finally, a factor graph model is constructed by integrating IMU pre-integration, laser odometry, visual odometry, and loop closure constraints, and iSAM2 incremental optimization is employed to achieve globally consistent mapping. Municipal tunnel tests show that the loop closure error is reduced to 0.096 m and the global reprojection error is 1.10 pixels, and the structural continuity of the constructed dense 3D map is significantly improved. This method provides a technical solution with centimeter-level accuracy for tunnel structural health monitoring, which is demonstrating strong practical potential for engineering applications.