Head tracking for the Oculus Rift

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.

[1]  R. Michael Buehrer,et al.  Handbook of Position Location: Theory, Practice and Advances , 2011 .

[2]  Jr. B. Smith Digital head tracking and position prediction for helmet mounted visual display systems , 1984 .

[3]  S. Sastry Nonlinear Systems: Analysis, Stability, and Control , 1999 .

[4]  Uwe H List,et al.  Nonlinear Prediction of Head Movements for Helmet-Mounted Displays , 1983 .

[5]  Ronald Azuma,et al.  Predictive tracking for augmented reality , 1995 .

[6]  K. Aminian,et al.  Quaternion-based fusion of gyroscopes and accelerometers to improve 3D angle measurement , 2006 .

[7]  A.-J. Baerveldt,et al.  A low-cost and low-weight attitude estimation system for an autonomous helicopter , 1997, Proceedings of IEEE International Conference on Intelligent Engineering Systems.

[8]  John Weston,et al.  Strapdown Inertial Navigation Technology, Second Edition , 2005 .

[9]  D. Titterton,et al.  Strapdown inertial navigation technology - 2nd edition - [Book review] , 2005, IEEE Aerospace and Electronic Systems Magazine.

[10]  Demoz Gebre-Egziabher,et al.  Calibration of Strapdown Magnetometers in Magnetic Field Domain , 2006 .

[11]  Steven M. LaValle,et al.  Sensing and Filtering: A Fresh Perspective Based on Preimages and Information Spaces , 2012, Found. Trends Robotics.

[12]  Walter Higgins,et al.  A Comparison of Complementary and Kalman Filtering , 1975, IEEE Transactions on Aerospace and Electronic Systems.

[13]  Greg Welch,et al.  Motion Tracking: No Silver Bullet, but a Respectable Arsenal , 2002, IEEE Computer Graphics and Applications.

[14]  S. Valiviita,et al.  Angular acceleration measurement: a review , 1998, IMTC/98 Conference Proceedings. IEEE Instrumentation and Measurement Technology Conference. Where Instrumentation is Going (Cat. No.98CH36222).