Best Axes Composition: Multiple Gyroscopes IMU Sensor Fusion to Reduce\n Systematic Error

In this paper, we propose an algorithm to combine multiple cheap Inertial\nMeasurement Unit (IMU) sensors to calculate 3D-orientations accurately. Our\napproach takes into account the inherent and non-negligible systematic error in\nthe gyroscope model and provides a solution based on the error observed during\nprevious instants of time. Our algorithm, the Best Axes Composition (BAC),\nchooses dynamically the most fitted axes among IMUs to improve the estimation\nperformance. We compare our approach with a probabilistic Multiple IMU (MIMU)\napproach, and we validate our algorithm in our collected dataset. As a result,\nit only takes as few as 2 IMUs to significantly improve accuracy, while other\nMIMU approaches need a higher number of sensors to achieve the same results.\n