Simultaneous localization and mapping (SLAM) is one of the core technologies to realize automated valet parking (AVP). Currently, advanced visual feature-based SLAM systems suffer from feature extraction failure and tracking loss due to the constraints of textureless scenes, unclear illumination, and dynamic conditions. To address these problems, this paper proposes a visual SLAM algorithm based on a semantic closed-loop detection algorithm using surround-view cameras and inertial measurement units (IMU) as sensors. The algorithm combines semantic features and the idea of inverse index to improve the traditional keyframes selection methods and the closed-loop detection algorithms, effectively avoiding the tedious and complicated feature point matching and improving the computational efficiency of the computer. Experiments show that the algorithm in this paper achieves better results in terms of precision and recall, absolute trajectory error (ATE), and relative pose error (RPE), and can meet the demand for SLAM and subsequent navigation in indoor parking lots.