Augmenting inertial motion capture with SLAM using EKF and SRUKF data fusion algorithms

• Published in: Measurement : journal of the International Measurement Confederation Vol. 222; p. 113690
• Main Authors: Azarbeik, Mohammad Mahdi, Razavi, Hamidreza, Merat, Kaveh, Salarieh, Hassan
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 30.11.2023
• Subjects: Extended and unscented Kalman filter; Inertial sensors; Motion capture; Sensor fusion; SLAM; Sensor fusion; Motion capture; Inertial sensors; SLAM; Extended and unscented Kalman filter
• ISSN: 0263-2241; 1873-412X
• DOI: 10.1016/j.measurement.2023.113690

This paper proposes quaternion-based extended and square-root unscented Kalman filters to estimate human body segment positions and orientations using low-cost IMUs in conjunction with a SLAM method. The Kalman filters use measurements based on SLAM output, multilink biomechanical constraints, and vertical referencing to correct errors. In addition to the sensor biases, the fusion algorithm is capable of estimating link geometries, allowing the imposition of biomechanical constraints without a priori knowledge of sensor positions. The proposed algorithms achieve up to 5.87 (cm) and 1.1 (deg) accuracy in position and attitude estimation in various scenarios of human arm movements. Compared to the EKF, the SRUKF algorithm presents a smoother and higher convergence rate but is 2.4 times more computationally demanding. After convergence, the SRUKF is up to 17% less and 36% more accurate than the EKF in position and attitude estimation, respectively. [Display omitted] •A low-cost, portable, easily used, and drift-free human body localization and pose estimation method.•Combining SLAM method and inertial sensors to estimate link positions.•Quaternion-based extended and square-root unscented Kalman filters pose estimation.•Estimating sensor biases & link geometries for use regardless of the sensor positions.