Improved Preintegration Method for Vehicle Navigation Based on Factor Graph Optimization

To solve the problem that the IMU nodes of the traditional factor graph are unable to be corrected in time and the positioning accuracy decreases in the condition of satellite failure, an improved preintegration method for the vehicle navigation system is proposed. Different from the current factor graph optimization method, the proposed method that involves corrective parameters of visual sensors is introduced. The structure of the system is changed when GNSS is unavailable and the calculated information of Lidar is used to compensate for IMU errors. The sliding window is used to improve the real-time performance of the system. The experimental results show that the proposed algorithm can improve the positioning accuracy of vehicle navigation when GNSS is unavailable. Compared with the traditional factor graph integrated navigation algorithm, the positioning accuracy of the proposed preintegration method is improved by more than 16% in GNSS failure environments.