基于多传感器的运动姿态测量算法

Orientation Estimation Algorithm for Motion Based on Multi-Sensor

在基于MEMS传感技术的运动姿态测量中,陀螺仪信号的漂移和载体线性加速度与重力加速度的叠加是影响测量结果准确性的主要原因,实践中一般采用静态补偿和滤波技术减小测量误差.基于自主研发的惯性测量单元,设计了一种新型两级扩展卡尔曼滤波器:基于四元数的运动姿态测量模型,首先构造自适应加速度误差协方差矩阵,消除载体线性加速度,再采用多传感器融合技术进行数据融合,修正陀螺仪信号漂移产生的误差.实验表明,本文算法结果与业界认可的动作捕捉系统Xsens的测量结果一致,可有效满足应用需求.

In the motion orientation estimation based on MEMS sensor technology, gyroscope signal drift error and gravity superimposed with linear acceleration are the two major reasons affecting the accuracy of estimation. In practice, static compensation and filter technology are commonly used to reduce the orientation estimation error. This paper designs a novel double stage extend Kalman Filter performed on self-developed inertial measurement unit. Above all, we construct adaptive acceleration error covariance matrix to eliminate the linear acceleration in quaternion-based orientation estimation model. Then, in order to correct the drift error produced by gyroscope, the multi-sensor data fusion technology is adopted to fuse the data. Experiment result indicates that the performance of our algorithm is in accordance with the motion capture system Xsens approbated widely. It proves that the algorithm can meet the application requirements effectively.