To solve the problem that a single sensor cannot provide continuous and stable position information. In this paper, an innovative method is proposed to build a high-precision positioning system by integrating three kinds of data, namely global satellite navigation system, Inertial Measurement Unit (IMU) and prior map point cloud matching, through the Error State Kalman Filter (ESKF) algorithm. At the same time, by introducing the correction matrix method, the pose problem can be corrected to avoid the system failing to obtain effective observations in the case of Global Navigation Satellite System (GNSS) failure or prior map matching delay, etc., so that the system can maintain stability and be in a high-precision state. The experimental results show that the proposed method can still maintain high precision in the case of failure of the observed value. Compared with the traditional single sensor positioning method and the common GNSS/IMU combined positioning method, the proposed method has higher robustness and reliability, and can better meet the positioning requirements of autonomous vehicles in complex environments.

Priori map  /  HD positioning  /  Multi-sensor fusion  /  Error State Kalman Filter (ESKF)