The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Inertial head-tracker sensor fusion by a complementary separate-bias Kalman filter
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Press, W. H., Teukolsky, S. A., Vetterling, W. T., & Flannery, B. P. (1994) · Cited by 15,411
introduction to random signals and applied kalman filtering
Brown, R. G., & Hwang, P. Y. (1992) · Virtual Defense Library (Ministerio de Defensa) · Cited by 3,365
Treatment of bias in recursive filtering
Friedland, B. (1969) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1969.1099223 · Cited by 744
Improving static and dynamic registration in an optical see-through HMD
Azuma, R., & Bishop, G. (1994) · Proceedings of the 21st annual conference on Computer graphics and interactive techniques - SIGGRAPH '94 · doi:10.1145/192161.192199 · Cited by 390
Meyer, K., Applewhite, H. L., & Biocca, F. A. (1992) · Presence: Teleoperators and Virtual Environments · doi:10.1162/pres.1992.1.2.173 · Cited by 239
Lawrence, A. (1998) · Mechanical Engineering Series · doi:10.1007/978-1-4612-1734-3 · Cited by 217
Mobile robot attitude estimation by fusion of inertial data
Vaganay, J., Aldon, M. J., & Fournier, A. (2002) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.1993.291995 · Cited by 158
Lawrence, A. W. (1993) · Springer eBooks · doi:10.1007/978-1-4684-0444-9 · Cited by 128
Survey of helmet tracking technologies
Ferrin, F. J. (1991) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.45422 · Cited by 65
Evaluation of a solid-state gyroscope for robotics applications
Barshan, B., & Durrant-Whyte, H. F. (1995) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/19.368102 · Cited by 60
Cited by
Azuma, R. T. (1997) · Presence: Teleoperators and Virtual Environments · doi:10.1162/pres.1997.6.4.355 · Cited by 10,121
(2009) · Cited by 701
Measuring orientation of human body segments using miniature gyroscopes and accelerometers
Luinge, H. J., & Veltink, P. H. (2005) · Medical & Biological Engineering & Computing · doi:10.1007/bf02345966 · Cited by 647
An extended Kalman filter for quaternion-based orientation estimation using MARG sensors
Marins, J. L., Yun, X., Bachmann, E. R., McGhee, R. B., & Zyda, M. J. (2002) · Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No.01CH37180) · doi:10.1109/iros.2001.976367 · Cited by 499
Estimating Three-Dimensional Orientation of Human Body Parts by Inertial/Magnetic Sensing
Sabatini, A. M. (2011) · Sensors · doi:10.3390/s110201489 · Cited by 349
Metadata
- Authors
- Foxlin, E.
- Venue
- Proceedings of the IEEE 1996 Virtual Reality Annual International Symposium
- Publisher
- IEEE
- Published
- 1996
- Pages
- 185-194
- OpenAlex
- W2532146065
- Cited by
- 365
- References
- 13
- FWCI
- 15.47
- Type
- conference paper
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Inertial head-tracker sensor fusion by a complementary separate-bias Kalman filter. https://app.cochat.ai/papers/10.1109/vrais.1996.490527
- The paper
- Foxlin, E. (1996). Inertial head-tracker sensor fusion by a complementary separate-bias Kalman filter. Proceedings of the IEEE 1996 Virtual Reality Annual International Symposium. https://doi.org/10.1109/vrais.1996.490527
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
