Inertial head-tracker sensor fusion by a complementary separate-bias Kalman filter

Current virtual environment and teleoperator applications are hampered by the need for an accurate, quick-responding head-tracking system with a large working volume. Gyroscopic orientation sensors can overcome problems with jitter, latency, interference, line-of-sight obscurations and limited range, but suffer from slow drift. Gravimetric inclinometers can detect attitude without drifting, but are slow and sensitive to transverse accelerations. This paper describes the design of a Kalman filter to integrate the data from these two types of sensors in order to achieve the excellent dynamic response of an inertial system without drift, and without the acceleration sensitivity of inclinometers.

[1]  Richard A. Brown,et al.  Introduction to random signals and applied kalman filtering (3rd ed , 2012 .

[2]  Frank Biocca,et al.  A Survey of Position Trackers , 1992, Presence: Teleoperators & Virtual Environments.

[3]  Devesh K Bhatnagar.  Position trackers for Head Mounted Display systems: A survey , 1993 .

[4]  Susumu Tachi,et al.  Compensation of time lag between actual and virtual spaces by multi-sensor integration , 1994, Proceedings of 1994 IEEE International Conference on MFI '94. Multisensor Fusion and Integration for Intelligent Systems.

[5]  Shmuel Merhav,et al.  Autonomously aided strapdown attitude reference system , 1991 .

[6]  George M. Siouris,et al.  Aerospace Avionics Systems: A Modern Synthesis , 1993 .

[7]  Kenneth R Britting,et al.  Inertial navigation systems analysis , 1971 .

[8]  Eric Michael Fuchs,et al.  Inertial head-tracking , 1993 .

[9]  Charles Broxmeyer,et al.  Inertial navigation systems , 1964 .

[10]  Eric Foxlin,et al.  An inertial head-orientation tracker with automatic drift compensation for use with HMD's , 1994 .

[11]  Richard H. Parvin.  Inertial navigation , 1962 .

[12]  B. E. Bona,et al.  Optimum Reset of Ship's Inertial Navigation System , 1966, IEEE Transactions on Aerospace and Electronic Systems.

[13]  Billur Barshan,et al.  Evaluation of a solid-state gyroscope for robotics applications , 1992 .

[14]  Michael Zyda,et al.  NPSNET: Flight Simulation Dynamic Modeling Using Quaternions , 1992, Presence: Teleoperators & Virtual Environments.

[15]  James L. Farrell,et al.  Integrated aircraft navigation , 2012 .

[16]  Frank J. Ferrin.  Survey of helmet tracking technologies , 1991, Medical Imaging.

[17]  Anthony Lawrence,et al.  Modern Inertial Technology , 1993 .

[18]  Ronald Azuma,et al.  Improving static and dynamic registration in an optical see-through HMD , 1994, SIGGRAPH.

[19]  Satoru Emura,et al.  Sensor fusion based measurement of human head motion , 1994, Proceedings of 1994 3rd IEEE International Workshop on Robot and Human Communication.

[20]  Marie-José Aldon,et al.  Mobile robot attitude estimation by fusion of inertial data , 1993, [1993] Proceedings IEEE International Conference on Robotics and Automation.

[21]  B. Friedland.  Treatment of bias in recursive filtering , 1969 .