Human motion estimation on Lie groups using IMU measurements
| Autor: | Vladimir Joukov, Ivan Petrović, Dana Kulic, Josip Cesic, Ivan Marković, Kevin Westermann |
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| Rok vydání: | 2017 |
| Předmět: |
0209 industrial biotechnology
Mathematical analysis Motion (geometry) Lie group 02 engineering and technology Kinematics human motion estimation Lie groups IMU Computer Science::Robotics Euler angles Extended Kalman filter symbols.namesake 020901 industrial engineering & automation Motion estimation Gimbal lock 0202 electrical engineering electronic engineering information engineering symbols 020201 artificial intelligence & image processing Rotation (mathematics) Mathematics |
| Zdroj: | IROS |
| DOI: | 10.1109/iros.2017.8206016 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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