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

Abstract

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.