Full body human motion estimation on Lie groups using 3D marker position measurements

Josip Ćesić, Vladimir Joukov, Ivan Petrović, Dana Kulić

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearch

10 Citations (Scopus)


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 languageEnglish
Title of host publication2016 IEEE-RAS 16th international Conference on Humanoid Robots (Humanoids 2016)
EditorsPaul Oh
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages8
ISBN (Electronic)9781509047185, 9781509047178
ISBN (Print)9781509047192
Publication statusPublished - 30 Dec 2016
Externally publishedYes
EventIEEE-RAS International Conference on Humanoid Robots 2016 - Cancun, Mexico
Duration: 15 Nov 201617 Nov 2016
Conference number: 16th

Publication series

NameIEEE-RAS International Conference on Humanoid Robots
PublisherIEEE, Institute of Electrical and Electronics Engineers
ISSN (Print)2164-0572
ISSN (Electronic)2164-0580


ConferenceIEEE-RAS International Conference on Humanoid Robots 2016
Abbreviated titleHumanoids 2016

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