Jiao Hongyuan, Xu Xiangbo, Chen Shao, Guo Ningyan, Yu Zhibin
School of Technology, Beijing Forestry University, Beijing 100083, China.
School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Sensors (Basel). 2024 Feb 5;24(3):1034. doi: 10.3390/s24031034.
High-accuracy heading angle is significant for estimating autonomous vehicle attitude. By integrating GNSS (Global Navigation Satellite System) dual antennas, INS (Inertial Navigation System), and a barometer, a GNSS/INS/Barometer fusion method is proposed to improve vehicle heading angle accuracy. An adaptive Kalman filter (AKF) is designed to fuse the INS error and the GNSS measurement. A random sample consensus (RANSAC) method is proposed to improve the initial heading angle accuracy applied to the INS update. The GNSS heading angle obtained by a dual-antenna orientation algorithm is additionally augmented to the measurement variable. Furthermore, the kinematic constraint of zero velocity in the lateral and vertical directions of vehicle movement is used to enhance the accuracy of the measurement model. The heading errors in the open and occluded environment are 0.5418° (RMS) and 0.636° (RMS), which represent reductions of 37.62% and 47.37% compared to the extended Kalman filter (EKF) method, respectively. The experimental results demonstrate that the proposed method effectively improves the vehicle heading angle accuracy.
高精度航向角对于估计自动驾驶车辆姿态具有重要意义。通过集成全球导航卫星系统（GNSS）双天线、惯性导航系统（INS）和气压计，提出了一种GNSS/INS/气压计融合方法来提高车辆航向角精度。设计了一种自适应卡尔曼滤波器（AKF）来融合INS误差和GNSS测量值。提出了一种随机抽样一致性（RANSAC）方法来提高应用于INS更新的初始航向角精度。通过双天线定向算法获得的GNSS航向角被额外添加到测量变量中。此外，利用车辆运动横向和垂直方向零速度的运动学约束来提高测量模型的精度。在开放和遮挡环境中的航向误差分别为0.5418°（均方根）和0.636°（均方根），与扩展卡尔曼滤波器（EKF）方法相比，分别降低了37.62%和47.37%。实验结果表明，所提方法有效地提高了车辆航向角精度。
