Lidar-IMU and Wheel Odometer Based Autonomous Vehicle Localization System

Localization is a critical issue in autonomous navigation and path planning. The traditional localization method for automatic driving is to use the GPS. But in many cases, such as near the high buildings, under the viaduct, in the basement or the tunnel, GPS signal will disappeared or be weakened, resulting in localization failure. This paper proposed a precise localization method based on Lidar, IMU and wheel odometer combined with point cloud data map matching. In this paper, information in the form of depth point cloud collected by Lidar is matched with the pre-known point cloud map data, and a Point-to-Plane Iterative Closest Point algorithm (PP-ICP) is introduced. In order to avoid the mismatch and localization failure, the start of the autonomous vehicle is set to the initial coordinates of the point cloud map data. Then, the predicted state equation which consists of the throttle control and steering wheel control of the autonomous vehicle is established. The first observation equation consists of the acceleration and angular velocity measured by the IMU. The second observation equation consists of the position and attitude measured by the Lidar matching point cloud, along with speed and distance measured by the wheel odometer. Finally, the extended Kalman filter is used to fuse the information data of the three sensors and thus update and correct the localization. Experiments using multi-sensor fusion were carried out in underground garages and the experimental results showed that the robustness and positioning accuracy can meet the engineering requirements.