Fast Complementary Filter for Attitude Estimation Using Low-Cost MARG Sensors

International audienceThis paper proposes a novel quaternion-basedattitude estimator with magnetic, angular rate, and gravity (MARG) sensor arrays. A new structure of a fixed-gaincomplementary filter is designed fusing related sensors. To avoidusing iterative algorithms, the accelerometer-based attitude determination is transformed into a linear system. Stable solutionto this system is obtained via control theory. With only onematrix multiplication, the solution can be computed. Using theincrement of the solution, we design a complementary filter thatfuses gyroscope and accelerometer together. The proposed filteris fast, since it is free of iteration. We name the proposed filter thefast complementary filter (FCF). To decrease significant effectsof unknown magnetic distortion imposing on the magnetometer, a stepwise filtering architecture is designed. The magneticoutput is fused with the estimated gravity from gyroscope andaccelerometer using a second complementary filter when thereis no significant magnetic distortion. Several experiments arecarried out on real hardware to show the performance andsome comparisons. Results show that the proposed FCF canreach the accuracy of Kalman filter. It successfully finds abalance between estimation accuracy and time consumption.Compared with iterative methods, the proposed FCF has muchless convergence speed. Besides, it is shown that the magneticdistortion would not affect the estimated Euler angles