基于预估测量值的EKF在手臂测姿中的应用

Extend Kalman Filter Based on Predicted Measurements

摘要:

针对载体线性加速度以及周围局部磁干扰对姿态测量精度的影响,基于已有的惯性测量单元,设计了一个基于四元数的实时估计手臂姿态的扩展卡尔曼滤波器(EKF)。提出利用四元数引入加速计和磁强计的预估测量值构造自适应测量噪声协方差阵的方法,结合QUEST算法,来判定姿态角解算对陀螺仪、加速计和磁强计输出信息的依赖程度,以此来提高测量精度。文末通过实验仿真对该方法进行了验证,并对实验结果和电磁跟踪系统采集到的数据进行了比较,结果表明,本文提出的方法能显著提高手臂姿态测量精度,可有效满足应用要求。

Abstract:

For the linear acceleration and magnetic interference that effect on the measurement precision of the attitude, based on the existing inertial measurement unit(IMU), this paper describes a quaternion-based extended Kalman filter designed for real-time estimation of the orientation of arm motion. And proposes an adaptive approach that constructs measurement noise covariance matrix by introducing the predicted measurements of the accelerator and magnetometer with quaternion, combining QUEST algorithms, to determine the degree of dependence of gyroscope, accelerometer and magnetometer output signals, in order to improve the measurement accuracy. At the end of this paper, this method is verified by simulation and experiment, and the comparison of experimental results and the data collected by electromagnetic tracking system, shows that the method proposed in this paper can significantly improve the arm pose measurement accuracy, which can which can meet application requirements effectively.