Best Axes Composition: Multiple Gyroscopes IMU Sensor Fusion to Reduce Systematic Error
This work addresses systematic error reduction in IMU sensor fusion for orientation estimation, which is incremental as it builds on existing MIMU approaches.
The paper tackles the problem of accurately calculating 3D-orientations using multiple cheap IMU sensors by addressing systematic errors in gyroscopes, resulting in significant accuracy improvements with as few as 2 IMUs compared to other methods requiring more sensors.
In this paper, we propose an algorithm to combine multiple cheap Inertial Measurement Unit (IMU) sensors to calculate 3D-orientations accurately. Our approach takes into account the inherent and non-negligible systematic error in the gyroscope model and provides a solution based on the error observed during previous instants of time. Our algorithm, the Best Axes Composition (BAC), chooses dynamically the most fitted axes among IMUs to improve the estimation performance. We compare our approach with a probabilistic Multiple IMU (MIMU) approach, and we validate our algorithm in our collected dataset. As a result, it only takes as few as 2 IMUs to significantly improve accuracy, while other MIMU approaches need a higher number of sensors to achieve the same results.