Effect of sensor noise characteristics and calibration errors on the choice of IMU-sensor fusion algorithms

Abstract

This paper focuses on accurate and precise orientation estimation with consumer-grade MEMS-IMUs for ‘slow’ orientation change and ‘short’-time applications. A simulation platform is developed to predict a suitable algorithm for a MEMS-IMU of known noise specifications, improving similar works. Experimentally measured noise characteristics of two commercial grade IMUs (MPU9250 and BNO055) are used in the simulation platform to generate simulated data and evaluate some popular orientation estimation algorithms along with two new Kalman filter-based algorithms. Real experiments are conducted with the same IMUs using an electromagnetic tracker as reference sensor. The output orientation results for two new improved algorithms are compared with other algorithms in simulations and real experiments. We show that the choice of the ‘best’ algorithm varies with the noise characteristics of individual sensors within the sensor module. The two new best-performing algorithms tested achieve<1˚ RMS angle error for the two low-cost consumer-grade IMUs.