Parameter estimation and hypothesis testing in linear models
Parameter estimation and hypothesis testing in linear models
Improving static and dynamic registration in an optical see-through HMD
SIGGRAPH '94 Proceedings of the 21st annual conference on Computer graphics and interactive techniques
Hybrid Tracking for Outdoor Augmented Reality Applications
IEEE Computer Graphics and Applications
Hybrid Inertial and Vision Tracking for Augmented Reality Registration
VR '99 Proceedings of the IEEE Virtual Reality
Inertial Head-Tracker Sensor Fusion by a Complimentary Separate-Bias Kalman Filter
VRAIS '96 Proceedings of the 1996 Virtual Reality Annual International Symposium (VRAIS 96)
A Hybrid Registration Method for Outdoor Augmented Reality
ISAR '01 Proceedings of the IEEE and ACM International Symposium on Augmented Reality (ISAR'01)
An Introduction to the Kalman Filter
An Introduction to the Kalman Filter
A Tracker Alignment Framework for Augmented Reality
ISMAR '03 Proceedings of the 2nd IEEE/ACM International Symposium on Mixed and Augmented Reality
Handling Uncertain Sensor Data in Vision-Based Camera Tracking
ISMAR '04 Proceedings of the 3rd IEEE/ACM International Symposium on Mixed and Augmented Reality
Ubiquitous Tracking for Augmented Reality
ISMAR '04 Proceedings of the 3rd IEEE/ACM International Symposium on Mixed and Augmented Reality
Predicting and estimating the accuracy of n-occular optical tracking systems
ISMAR '06 Proceedings of the 5th IEEE and ACM International Symposium on Mixed and Augmented Reality
Spatial relationship patterns: elements of reusable tracking and calibration systems
ISMAR '06 Proceedings of the 5th IEEE and ACM International Symposium on Mixed and Augmented Reality
A System Architecture for Ubiquitous Tracking Environments
ISMAR '07 Proceedings of the 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality
Supporting outdoor mixed reality applications for architecture and cultural heritage
SpringSim '10 Proceedings of the 2010 Spring Simulation Multiconference
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Ubiquitous Tracking (Ubitrack) setups, consisting of many previously unknown sensors, offer many possibilities to perform sensor fusion in order to increase robustness and accuracy. In particular, the dynamic combination of mobile and stationary trackers enables the creation of new wide-area tracking concepts. In this work, we present a setup in which a gyroscope is dynamically fused with three different mobile and stationary sensors, based on the concepts of Spatial Relationship Graphs (SRGs) and Patterns. For this, we contribute new patterns that, based on well-known algorithms, enable the transformation of rotation velocity and the fusion with different absolute trackers. The usefulness of the approach is shown in a system that automatically reconfigures the SRG based on course tracking data, and, depending on the structure of this SRG, automatically selects a suitable fusion algorithm.