  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.
