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Supervised Learning for Yaw Orientation Estimation

: Feigl, T.; Mutschler, C.; Philippsen, M.


Institute of Electrical and Electronics Engineers -IEEE-:
IPIN 2018, Ninth International Conference on Indoor Positioning and Indoor Navigation : September 24-27, 2018, Nantes, France
Piscataway, NJ: IEEE, 2018
ISBN: 978-1-5386-5635-8
ISBN: 978-1-5386-5636-5
International Conference on Indoor Positioning and Indoor Navigation (IPIN) <9, 2018, Nantes>
Fraunhofer IIS ()

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.