Precise and robust sideslip angle estimation based on INS/GNSS integration using invariant extended Kalman filter
Zhihuang Zhang, Jintao Zhao, Changyao Huang, Liang Li
Abstract
Sideslip angle estimation is vital to the safety and active control of autonomous vehicles. In this paper, an innovative vehicle kinematic-based sideslip angle estimation method is proposed. The method is built on multi-sensor fusion, which fuses the information of inertial measurement unit, global navigation satellite system (GNSS), and onboard sensors to continuously estimate the attitude and velocity of the vehicle and thus obtain the sideslip angle. The invariant extended Kalman filter framework is adopted and the left-invariant form is derived to implement the GNSS measurement update. In order to solve the unobservability problem when only a single GNSS receiver is equipped, the vehicle motion constraint is introduced into the filter to improve the accuracy of heading angle estimation. The method is validated by field test and the results show that the method is robust in terms of converge efficiency. Furthermore, the sideslip angle estimation accuracy is satisfactory with the average absolute error less than 0.25°, which meets the active safety control requirements for autonomous driving.