Sensor fusion algorithms for orientation tracking via magnetic and inertial measurement units: An experimental comparison survey

• Published in: Information fusion Vol. 76; pp. 8 - 23
• Main Authors: Nazarahari, Milad, Rouhani, Hossein
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-12-2021
• Subjects: Adaptive gain tuning; Attitude and heading reference system; Complementary filter; Linear/extended/complementary/unscented/cubature Kalman filter; Magnetic and inertial measurement units; Sensor fusion algorithm; Complementary filter; Magnetic and inertial measurement units; Attitude and heading reference system; Adaptive gain tuning; Linear/extended/complementary/unscented/cubature Kalman filter; Sensor fusion algorithm
• ISSN: 1566-2535; 1872-6305
• DOI: 10.1016/j.inffus.2021.04.009

•Compared 36 sensor fusion algorithms for orientation tracking with MIMUs.•Implemented optimal adaptive gain tuning for sensor fusion algorithms.•Shared sample data and all codes of the implemented sensor fusion algorithms.•Identified sensor fusion algorithms with the highest accuracy in various families.
Lightweight and low-cost wearable magnetic and inertial measurement units (MIMUs) have found numerous applications, such as aerial vehicle navigation or human motion analysis, where the 3D orientation tracking of a rigid body is of interest. However, due to the errors in measurements of gyroscope, accelerometer, and/or magnetometer inside a MIMU, numerous studies have proposed sensor fusion algorithms (SFAs) to estimate the 3D orientation accurately and robustly. This paper contributes to these efforts by performing an experimental comparison among a variety of SFAs. Notably, we compared the estimated orientation of 36 SFAs from the complementary filter and linear/extended/complementary/unscented/cubature Kalman filter families with the reference orientation obtained from a camera motion-capture system. The experimental study included data collection with a foot-worn MIMU where nine participants performed various short- and long-duration tasks. We shared the codes and sample of data in https://www.ncbl.ualberta.ca/codes to enable other researchers to compare their works with the literature toward creating a comprehensive online repository for SFAs. To perform a fair comparison, we used the Particle Swarm Optimization routine to find the optimal adaptive gain tuning scheme for each SFAs, as recommended in the literature. Our experimental results showed that gyroscope static bias removal, in general, showed to be effective in reducing the estimation error of SFAs, specifically during long-duration trials. Moreover, our experimental results identified the SFAs with the highest accuracy from each family. We also reported the execution times for the selected SFAs from each family. This paper is among the first experimental comparison studies which provide such breadth of coverage across various SFAs for tracking orientation with MIMUs.