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 real-time attitude estimation, including factored quaternion algorithm (FQA) for static attitude estimation, and Kalman filtering fordata 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 nonlinear acceleration, and improves attitude estimation accuracy. Experimental results show that the R-adapted Kalman filter can accurately estimate attitudes in real-time. Additionally, 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%.
ZHANG Jin-yi1,2,3, XU De-zheng2, LI Ruo-han1,CHEN Xing-xiu1, XU Qin-le2
. 9-axis MEMS-IMU real-time data fusion algorithm for attitude estimation[J]. Journal of Shanghai University, 2015
, 21(5)
: 547
-559
.
DOI: 10.3969/j.issn.1007-2861.2014.01.037
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