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Multiple rigidly attached Inertial Measurement Unit (IMU) sensors provide aricher flow of data compared to a single IMU. State-of-the-art methods follow aprobabilistic model of IMU measurements based on the random nature of errorscombined under a Bayesian framework. However, affordable low-grade IMUs, inaddition, suffer from systematic errors due to their imperfections not coveredby their corresponding probabilistic model. In this paper, we propose a method,the Best Axes Composition (BAC) of combining Multiple IMU (MIMU) sensors datafor accurate 3D-pose estimation that takes into account both random andsystematic errors by dynamically choosing the best IMU axes from the set of allavailable axes. We evaluate our approach on our MIMU visual-inertial sensor andcompare the performance of the method with a purely probabilisticstate-of-the-art approach of MIMU data fusion. We show that BAC outperforms thelatter and achieves up to 20% accuracy improvement for both orientation andposition estimation in open loop, but needs proper treatment to keep theobtained gain.

Best Axes Composition Extended: Multiple Gyroscopes and Accelerometers Data Fusion to Reduce Systematic Error