A Simulation-Based Efficient Optimization Method of an Odometry Localization Filter for Vehicles With Increased Maneuverability

With the increasing level of driving automation, localization and navigation are not only used to provide positioning and route guidance information for users, but are also important inputs for vehicle control. Odometry localization method is the most widely used localization method due to its good short-term accuracy and cost-effectiveness, despite its known limitations like drift and environment dependency. Optimizing odometry remains a valuable area of research. By using the UKF-based odometry localization methode for vehicles with increased maneuverability introduced in our previous work, this paper presents a simulation-based optimization method to improve the accuracy of the odometry. This proposed simulation-based optimization method aims to achieve the accuracy goal with low computation effort. The covariance matrices of the UKF-based odometry are optimized by the particle swarm algorithm. In order to make the in simulation optimized covariance also applicable in the real vehicle, sensor error models are built up to generate realistic sensor signals. To reduce the computation effort during optimization an efficient driving maneuver, which covers more vehicle states is generated and used instead of normal parking maneuvers. The use of the efficient driving maneuver has been shown to reduce the optimization effort by approximately 60% without sacrifice the optimization effect. The efficacy of the optimized covariance matrices in enhancing odometry accuracy has been validated in both simulated and real-driving tests. The optimized odometry can reach an average end position error of 11cm and average end orientation error of 0.4∘. Furthermore, a sensitivity analysis of sensor accuracy and noise level on odometry has been performed in the simulation environment with the help of the proposed optimization methods. Odometry using sensors of various accuarcy and noise levels are optimized to achieve its best performance. The simulation results indicate the importance of the IMU sensor in the odometry localization method. This conclusion is supported by the results of a real driving test that used two IMU sensors with different accuracy and noise levels. The results of the sensitivity analysis provides a basis for sensor selection in vehicle system design.