Human motion estimation on Lie groups using IMU measurements (CROSBI ID 649924)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Joukov, Vladimir ; Ćesić, Josip ; Westermann, Kevin ; Marković, Ivan ; Kulić, Dana ; Petrović, Ivan
engleski
Human motion estimation on Lie groups using IMU measurements
This paper proposes a new algorithm for human motion estimation using inertial measurement unit (IMU) measurements. We model the joints by matrix Lie groups, namely the special orthogonal groups SO(2) and SO(3), representing rotations in 2D and 3D space, respectively. The state space is defined by the Cartesian product of the rotation groups and their velocities and accelerations, given a kinematic model of the articulated body. In order to estimate the state, we propose the Lie Group Extended Kalman Filter (LG-EKF), thus explicitly accounting for the non-Euclidean geometry of the state space, and we derive the LG-EKF recursion for articulated motion estimation based on IMU measurements. The performance of the proposed algorithm is compared to the EKF based on Euler angle parametrization in both simulation and real-world experiments. The results show that the proposed filter is a significant improvement over the Euler angles based EKF, since it estimates motion more accurately and is not affected by gimbal lock.
human motion estimation, Lie groups, IMU
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
Podaci o prilogu
2017.
objavljeno
Podaci o matičnoj publikaciji
Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017)
Podaci o skupu
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017)
predavanje
24.09.2017-29.09.2017
Vancouver, Kanada