Head tracking for the Oculus Rift

Steven M. Lavalle, Anna Yershova, Max Katsev, Michael Antonov

Research output: Contribution to journalConference article

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 languageEnglish (US)
Article number6906608
Pages (from-to)187-194
Number of pages8
JournalProceedings - IEEE International Conference on Robotics and Automation
DOIs
StatePublished - Sep 22 2014
Event2014 IEEE International Conference on Robotics and Automation, ICRA 2014 - Hong Kong, China
Duration: May 31 2014Jun 7 2014

Fingerprint

Gyroscopes
Virtual reality
MEMS
Gravitation
Magnetic fields
Sensors
Costs
Compensation and Redress

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 journalConference article

Lavalle, Steven M. ; Yershova, Anna ; Katsev, Max ; Antonov, Michael. / Head tracking for the Oculus Rift. In: Proceedings - IEEE International Conference on Robotics and Automation. 2014 ; pp. 187-194.
@article{277a8c0428ee47e08d0c57cc9bd59ee8,
title = "Head tracking for the Oculus Rift",
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.",
author = "Lavalle, {Steven M.} and Anna Yershova and Max Katsev and Michael Antonov",
year = "2014",
month = "9",
day = "22",
doi = "10.1109/ICRA.2014.6906608",
language = "English (US)",
pages = "187--194",
journal = "Proceedings - IEEE International Conference on Robotics and Automation",
issn = "1050-4729",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

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 -