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As one of the most challenging driving tasks, parking is a common but particularly troublesome problem in large cities. Recently, an excellent solution-automated valet parking (AVP) has become a hot research topic, which allows the driver to leave the vehicle in a drop-off area, while the vehicle driving into the parking slot by itself. For AVP, the precise localization is an indispensable module. However, the global positioning system (GPS) cannot be used in the underground parking lot and the localization method based on lidar is too expensive. In response to solve this problem, we propose a simultaneous localization and mapping system with the semantic information of parking slots (PS-SLAM), which is based on visual-inertial and around view images. First, the calibration of multi-sensors is conducted to obtain their intrinsic and extrinsic parameters. In this way, the around view image and transformation matrices between sensors can be acquired.