Abstract
We present methods for efficiently maintaining human head orientation using low-cost MEMS sensors. We particularly address gyroscope integration and compensation of dead reckoning errors using gravity and magnetic fields. Although these problems have been well-studied, our performance criteria are particularly tuned to optimize user experience while tracking head movement in the Oculus Rift Development Kit, which is the most widely used virtual reality headset to date. We also present novel predictive tracking methods that dramatically reduce effective latency (time lag), which further improves the user experience. Experimental results are shown, along with ongoing research on positional tracking.
| Original language | English (US) |
|---|---|
| Article number | 6906608 |
| Pages (from-to) | 187-194 |
| Number of pages | 8 |
| Journal | Proceedings - IEEE International Conference on Robotics and Automation |
| DOIs | |
| State | Published - Sep 22 2014 |
| Event | 2014 IEEE International Conference on Robotics and Automation, ICRA 2014 - Hong Kong, China Duration: May 31 2014 → Jun 7 2014 |
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ASJC Scopus subject areas
- Software
- Control and Systems Engineering
- Artificial Intelligence
- Electrical and Electronic Engineering
Cite this
Head tracking for the Oculus Rift. / Lavalle, Steven M.; Yershova, Anna; Katsev, Max; Antonov, Michael.
In: Proceedings - IEEE International Conference on Robotics and Automation, 22.09.2014, p. 187-194.Research output: Contribution to journal › Conference article
}
TY - JOUR
T1 - Head tracking for the Oculus Rift
AU - Lavalle, Steven M.
AU - Yershova, Anna
AU - Katsev, Max
AU - Antonov, Michael
PY - 2014/9/22
Y1 - 2014/9/22
N2 - We present methods for efficiently maintaining human head orientation using low-cost MEMS sensors. We particularly address gyroscope integration and compensation of dead reckoning errors using gravity and magnetic fields. Although these problems have been well-studied, our performance criteria are particularly tuned to optimize user experience while tracking head movement in the Oculus Rift Development Kit, which is the most widely used virtual reality headset to date. We also present novel predictive tracking methods that dramatically reduce effective latency (time lag), which further improves the user experience. Experimental results are shown, along with ongoing research on positional tracking.
AB - We present methods for efficiently maintaining human head orientation using low-cost MEMS sensors. We particularly address gyroscope integration and compensation of dead reckoning errors using gravity and magnetic fields. Although these problems have been well-studied, our performance criteria are particularly tuned to optimize user experience while tracking head movement in the Oculus Rift Development Kit, which is the most widely used virtual reality headset to date. We also present novel predictive tracking methods that dramatically reduce effective latency (time lag), which further improves the user experience. Experimental results are shown, along with ongoing research on positional tracking.
UR - http://www.scopus.com/inward/record.url?scp=84926460499&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84926460499&partnerID=8YFLogxK
U2 - 10.1109/ICRA.2014.6906608
DO - 10.1109/ICRA.2014.6906608
M3 - Conference article
AN - SCOPUS:84926460499
SP - 187
EP - 194
JO - Proceedings - IEEE International Conference on Robotics and Automation
JF - Proceedings - IEEE International Conference on Robotics and Automation
SN - 1050-4729
M1 - 6906608
ER -