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

TL;DR

This paper introduces the Best Axes Composition algorithm that fuses multiple low-cost IMU sensors, dynamically selecting axes to reduce systematic errors and enhance 3D orientation accuracy with fewer sensors.

Contribution

The paper presents a novel dynamic axes selection algorithm for IMU fusion that effectively reduces systematic errors and improves orientation estimation with fewer sensors.

Findings

Only 2 IMUs needed for significant accuracy improvement

BAC outperforms probabilistic MIMU approaches with fewer sensors

Validated on collected dataset showing enhanced performance

Abstract

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.