Visual inertial simultaneous localization and mapping (SLAM) technology improves the accuracy of mapping and positioning by considering relevant visual and inertial constraints. However, in low-light environments, the quality of feature point extraction and tracking stability at the visual front-end are poor, which leads to easy loss of tracking and low positioning accuracy in the visual inertial SLAM algorithm. Therefore, we propose a monocular inertial SLAM algorithm called GS-VINS based on the VINS-Mono framework. First, an adaptive image enhancement algorithm is used to enhance the grayscale distribution of low-light images. Then, a GN2_SuperPoint feature point detection network is proposed, and it is combined with a feature point dynamic tracking module to improve the stability of optical flow tracking. Experiments on the EuRoC dataset and in real-world scenarios show that the proposed algorithm improves localization accuracy by 26.57% compared to VINS-Mono and it demonstrates strong robustness to changes in lighting. In the comparison experiment, the success rate of the feature tracking increases by 8%, and the closure error in real-world scenarios is reduced by ~45.73%. The proposed algorithm shows good accuracy and stability in low-light environments and provides a novel solution for visual navigation under low-light conditions, thereby offering valuable engineering applications.