Liu Mei, Cai Yuanli, Zhang Lihao, Wang Yiqun
School of Automation Science and Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
Micromachines (Basel). 2021 Nov 8;12(11):1373. doi: 10.3390/mi12111373.
In robot inertial navigation systems, to deal with the problems of drift and noise in the gyroscope and accelerometer and the high computational cost when using extended Kalman filter (EKF) and particle filter (PF), a complementary filtering algorithm is utilized. By combining the Inertial Measurement Unit (IMU) multi-sensor signals, the attitude data are corrected, and the high-precision attitude angles are obtained. In this paper, the quaternion algorithm is used to describe the attitude motion, and the process of attitude estimation is analyzed in detail. Moreover, the models of the sensor and system are given. Ultimately, the attitude angles are estimated by using the quaternion extended Kalman filter, linear complementary filter, and Mahony complementary filter, respectively. The experimental results show that the Mahony complementary filtering algorithm has less computational cost than the extended Kalman filtering algorithm, while the attitude estimation accuracy of these two algorithms is similar, which reveals that Mahony complementary filtering is more suitable for low-cost embedded systems.
在机器人惯性导航系统中，为了解决陀螺仪和加速度计中的漂移和噪声问题以及使用扩展卡尔曼滤波器（EKF）和粒子滤波器（PF）时的高计算成本，采用了一种互补滤波算法。通过组合惯性测量单元（IMU）多传感器信号，校正姿态数据，并获得高精度姿态角。本文采用四元数算法描述姿态运动，并详细分析了姿态估计过程。此外，给出了传感器和系统的模型。最终，分别使用四元数扩展卡尔曼滤波器、线性互补滤波器和马奥尼互补滤波器估计姿态角。实验结果表明，马奥尼互补滤波算法的计算成本低于扩展卡尔曼滤波算法，而这两种算法的姿态估计精度相似，这表明马奥尼互补滤波更适合低成本嵌入式系统。
