A linear Kalman Filtering-based approach for 3D orientation estimation from Magnetic/Inertial sensors

The accurate estimation of the three dimensional (3D) orientation estimation from Magnetic/Inertial Measurement Units (MIMUs) is a challenging task due to the noisiness of the sensor data and the non-linearity of the measurement models. Recently, new linear Kalman Filtering-based (KF) estimators have been presented in literature which address the tilt angles estimation problem (i.e. the pitch and roll angles, or the attitude) as a source separation technique applied to the accelerometer signal. In this paper one of these methods is extended to the magnetometer signal, under the assumption of hard-iron magnetic errors. The Earth's magnetic field is then estimated in a linear KF framework to provide an additional reference for heading estimation, yielding full 3D orientation estimation. The proposed method was validated on data from a body-worn MIMU. Five subjects and two scenarios were included in the experimental validation. The proposed KF lowered the magnetic errors to less than 4 μT, with corresponding orientation errors that ranged from 2.8° (attitude) to 8.5° (heading).