Abstract
This paper proposes a new algorithm for full body human motion estimation using 3D marker position measurements. The joints are represented with Lie group members, including special orthogonal groups SO(2) and SO(3), and a special euclidean group SE(3). We employ the Lie Group Extended Kalman Filter (LG-EKF) for stochastic inference on groups, thus explicitly accounting for the non-euclidean geometry of the state space, and provide the derivation of the LG-EKF recursion for articulated motion estimation. We evaluate the performance of the proposed algorithm in both simulation and on real-world motion capture data, comparing it with the Euler angles based EKF. The results show that the proposed filter significantly outperforms the Euler angles based EKF, since it estimates human motion more accurately and is not affected by gimbal lock.
Original language | English |
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Title of host publication | 2016 IEEE-RAS 16th international Conference on Humanoid Robots (Humanoids 2016) |
Editors | Paul Oh |
Place of Publication | Piscataway NJ USA |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
Pages | 826-833 |
Number of pages | 8 |
ISBN (Electronic) | 9781509047185, 9781509047178 |
ISBN (Print) | 9781509047192 |
DOIs | |
Publication status | Published - 30 Dec 2016 |
Externally published | Yes |
Event | IEEE-RAS International Conference on Humanoid Robots 2016 - Cancun, Mexico Duration: 15 Nov 2016 → 17 Nov 2016 Conference number: 16th |
Publication series
Name | IEEE-RAS International Conference on Humanoid Robots |
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Publisher | IEEE, Institute of Electrical and Electronics Engineers |
ISSN (Print) | 2164-0572 |
ISSN (Electronic) | 2164-0580 |
Conference
Conference | IEEE-RAS International Conference on Humanoid Robots 2016 |
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Abbreviated title | Humanoids 2016 |
Country/Territory | Mexico |
City | Cancun |
Period | 15/11/16 → 17/11/16 |