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

• Published in: Sensors and actuators. A. Physical. Vol. 379; p. 115850
• Main Authors: Harindranath, Aparna, Arora, Manish
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2024
• Subjects: consumer-grade MEMS-IMU; MEMS-IMU Noise effects; MEMS-IMU orientation estimation algorithm; MEMS-IMU sensor fusion; MEMS-IMU Noise effects; MEMS-IMU orientation estimation algorithm; MEMS-IMU sensor fusion; MEMS-IMU; consumer-grade MEMS-IMU
• ISSN: 0924-4247
• DOI: 10.1016/j.sna.2024.115850

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. [Display omitted] •Accurate and precise orientation estimation with consumer-grade MEMS-IMUs.•Orientation estimated for ‘slow’ orientation change and ‘short’-time applications.•Simulation platform to set a best algorithm for IMU of known noise specifications.•Two Kalman filter-based algorithms proposed achieve<1˚ RMS angle error for two IMUs.•Choice of ‘best’ algorithm varies with noise characteristics of individual sensors.