Best Axes Composition: Multiple Gyroscopes IMU Sensor Fusion to Reduce Systematic Error release_zh6ggadwljhc5gc64sm4eaacnm

by Marsel Faizullin, Gonzalo Ferrer

Released as a article .

2021  

Abstract

In this paper, we have proposed 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 Axis Composition (BAC), chooses dynamically the most fitted axes among IMUs to improve the estimation performance. We have compared our approach with a probabilistic Multiple IMU (MIMU) approach, and we have validated 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.
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Date   2021-07-06
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arXiv  2107.02632v1
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