Supervised Learning for Yaw Orientation Estimation

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Details zur Publikation

Autor(en): Feigl T, Mutschler C, Philippsen M
Verlag: IEEE Xplore
Jahr der Veröffentlichung: 2018
Tagungsband: Proceedings of the 9th International Conference on Indoor Positioning and Indoor Navigation (IPIN 2018)
ISBN: 978-1-5386-5635-8
ISSN: 2471-917X
Sprache: Englisch


Abstract

With free movement and multi-user capabilities, there is demand to open up Virtual Reality (VR) for large spaces. However, the cost of accurate camera-based tracking grows with the size of the space and the number of users. No-pose (NP) tracking is cheaper, but so far it cannot accurately and stably estimate the yaw orientation of the user’s head in the long-run.

Our novel yaw orientation estimation combines a single inertial sensor located at the human’s head with inaccurate positional tracking. We exploit that humans tend to walk in their viewing direction and that they also tolerate some orientation drift. We classify head and body motion and estimate heading drift to enable low-cost long-time stable head orientation in NP tracking on 100 m × 100 m. Our evaluation shows that we estimate heading reasonably well.


FAU-Autoren / FAU-Herausgeber

Feigl, Tobias
Lehrstuhl für Informatik 2 (Programmiersysteme)
Mutschler, Christopher Dr.-Ing.
Lehrstuhl für Informatik 14 (Maschinelles Lernen und Datenanalytik)
Philippsen, Michael Prof. Dr.
Lehrstuhl für Informatik 2 (Programmiersysteme)


Zitierweisen

APA:
Feigl, T., Mutschler, C., & Philippsen, M. (2018). Supervised Learning for Yaw Orientation Estimation. In Proceedings of the 9th International Conference on Indoor Positioning and Indoor Navigation (IPIN 2018). Nantes, FR: IEEE Xplore.

MLA:
Feigl, Tobias, Christopher Mutschler, and Michael Philippsen. "Supervised Learning for Yaw Orientation Estimation." Proceedings of the 9th International Conference on Indoor Positioning and Indoor Navigation (IPIN 2018), Nantes IEEE Xplore, 2018.

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Zuletzt aktualisiert 2019-11-02 um 10:38