Localization is a key problem for autonomous driving in indoor parking. There have been some previously proposed methods based on UWB, LiDAR, fisheye cameras, etc. However, most of these methods have some drawbacks such as high cost or dependency on light conditions. To address these challenges, this paper proposes a novel Time-of-Flight (ToF) camera based mapping and localization system for indoor parking lots leveraging Manhattan World Regulation. ToF cameras are low-cost and can actively generate dense point clouds of the environment without external light sources. To overcome the shortcoming of ToF camera small field of view, the proposed system utilizes the structural information of the ceiling of indoor parking lots and Manhattan World Regulation. We track the surface normals on the unit sphere for drift-free rotation estimation. Based on this drift-free rotation, we can effectively calculate 6-DOF pose with decoupled rotation and translation estimation during mapping or global localization. This new system runs in real-time on limited computation resources and is demonstrated on two different challenging indoor parking, achieving real-time performance at 10 Hz and localization error less than 0.1 meter.