9轴MEMS-IMU实时姿态估算算法

9-axis MEMS-IMU real-time data fusion algorithm for attitude estimation

随着对微机电系统一惯性测量单元(micro-electro-mechanical system-inertial measurement unit,MEMS-IMU)在室内定位、动态追踪等应用领域中的需求日益迫切,使得具有高精度、低成本和实时性的MEMS-IMU模块设计成为研究热点.针对MEMS-IMU的核心技术——姿态估算进行研究,设计了一种基于四元数的9轴MEMS-IMU实时姿态估算算法.该算法运用分解四元数算法处理加速度和磁感应强度数据,计算出静态四元数;通过角速度与四元数的微分关系估算动态四元数;运用卡尔曼滤波融合动、静态四元数,进而实现实时姿态估算.针对分解四元数算法中存在的奇异值问题,提出了转轴补偿方法对其修正,以实现全姿态估算;考虑动态情况下的非线性加速度分量对姿态估算精度的影响,设计了R自适应卡尔曼滤波器,以进一步提高姿态估算算法的精度.验证结果表明,R自适应卡尔曼滤波器能够有效抑制加速度噪声,提高姿态估算精度;同时,转轴补偿-分解四元数算法能够准确估算奇异值点的姿态信息,并且计算时间仅为原"借角"补偿方法的50%左右,有效提高了整体算法的实时性.

To meet urgent application demands in indoor location and motion tracking, studies on low-cost high-resolution and real-time micro-electro-mechanical system-inertial measurement unit （MEMS-IMU） have attracted much attention. This paper presents a quaternion-based data fusion algorithm for reaLtime attitude estimation, including fac- tored quaternion algorithm （FQA） for static attitude estimation, and Kalman filtering for data fusion. A singularity avoidance method, axis-exchanged compensation, is proposed to modify the FQA, allowing the algorithm to track at all attitudes. An R-adapted module is designed to adjust the Kalman gain, which effectively restrains noise due to dynamic non- linear acceleration, and improves attitude estimation accuracy. Experimental results show that the R-adapted Kalman filter can accurately estimate attitudes in real-time. Addition- ally, FQA with an axis-exchanged method has good performance in estimating attitudes of singularity points, and the computational efficiency is higher than a previous method by 50%.