A DCM Based Attitude Estimation Algorithm for Low-Cost MEMS IMUs

Abstract

An attitude estimation algorithm is developed using an adaptive extended Kalman filter for low-cost microelectromechanicalsystem (MEMS) triaxial accelerometers and gyroscopes, that is, inertial measurement units (IMUs). Although these MEMS sensors are relatively cheap, they give more inaccurate measurements than conventional high-quality gyroscopes and accelerometers. To be able to use these low-cost MEMS sensors with precision in all situations, a novel attitude estimation algorithm is proposed for fusing triaxial gyroscope and accelerometer measurements. An extended Kalman filter is implemented to estimate attitude in direction cosine matrix (DCM) formation and to calibrate gyroscope biases online. We use a variable measurement covariance for acceleration measurements to ensure robustness against temporary nongravitational accelerations, which usually induce errors when estimating attitude with ordinary algorithms. The proposed algorithm enables accurate gyroscope online calibration by using only a triaxial gyroscope and accelerometer. It outperforms comparable state-of-the-art algorithms in those cases when there are either biases in the gyroscope measurements or large temporary nongravitational accelerations present. A low-cost, temperature-based calibration method is also discussed for initially calibrating gyroscope and acceleration sensors. An open source implementation of the algorithm is also available.

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“…The mpu-6050 devices have an onboard Digital Motion Processor6 [19], which processes complex 6-axis Motion Fusion algorithms, but we have conducted a test which reveals that such algorithms may not be suitable in this study for the unstable monitoring data. Several studies [20][21][22][23][24][25] adopted and developed types of algorithms to obtain better results in terms of acceleration. In this study, we process the signal with the Kalman filter by an onboard STM32 processor (STM8S003); the processed data present the desirable accuracy.…”

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“…For calibration of the MEMS IMU, a temperature based calibration method by Hyyti and Visala (2015) is utilized. The measurement model is formulated as (2) which is dependent on temperature T.…”

“…The algorithm should also correct gyroscope bias online. According to Hyyti and Visala (2015), there are only a few accurate algorithms that can estimate gyroscope bias online with only triaxial gyroscope and accelerometer. As their implementation is the only one freely available, it is selected for this work.…”

“…As their implementation is the only one freely available, it is selected for this work. Hyyti and Visala (2015) use a direct cosine matrix (DCM) based adaptive extended Kalman filter (EKF) for attitude estimation with low-cost MEMS IMUs. The attitude is estimated only partially as only the last row vector of the DCM (corresponding to the gravity vector) is used as one of the states of the sensor fusion algorithm.…”

“…This minimises errors caused by transient non-gravitational accelerations (head movement) in the attitude estimate. The measurement covariance is formulated in (Hyyti and Visala, 2015) as follows: , ) ( …”