Best Axes Composition: Multiple Gyroscopes IMU Sensor Fusion to Reduce Systematic Error
release_zh6ggadwljhc5gc64sm4eaacnm
by
Marsel Faizullin, Gonzalo Ferrer
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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