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

2021-07-06 14:16:07
Marsel Faizullin, Gonzalo Ferrer
arXiv_RO
     
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 {\em 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.

Abstract (translated)

URL

https://arxiv.org/abs/2107.02632

PDF

https://arxiv.org/pdf/2107.02632.pdf

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