High-precision and real-time ego-motion estimation is vital for autonomous vehicle. There is a lot GPS-denied maneuver such as underground parking lot in urban areas. Therefore, the localization system relying solely on GPS cannot meets the requirements. Recently, lidar odometry and visual odometry have been introduced into localization systems to overcome the problem of missing GPS signals. Compared with visual odometry, lidar odometry is not susceptible to light, which is widely applied in weak-light environments. Besides, the autonomous parking is highly dependent on the geometric information around the vehicle, which makes building map of surroundings essential for autonomous vehicle. We propose a lidar inertial odometry and mapping. By sensor fusion, we compensate for the drawback of applying a single sensor, allowing the system to provide a more accurate estimate. Compared to other odometry using IMU and lidar, we apply a tight coupled of lidar and IMU method to achieve lower drift, which can effectively overcome the degradation problem based on pure lidar method, ensuring precise pose estimation in fast motion. In addition, we propose a map update method to ensure the real-time performance of the mapping, which meets the high requirements of the autonomous parking in the dynamic environment. At the back end, we apply a global pose optimization to reduce the drift of the pose over time. The experimental results demonstrate that the proposed odometry can stably estimate the pose in high dynamic and degraded underground parking lot in real-time. Besides, the proposed mapping method can update the map stably and reduce the drift of pose over time.