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Fundamentals of Ubiquitous Tracking for Augmented Reality

Fundamentals of Ubiquitous Tracking for Augmented Reality. Vienna University of Technology Joe Newman, Thomas Pintaric, Dieter Schmalstieg Technische Universität München Martin Wagner, Asa MacWilliams, Martin Bauer, Gudrun Klinker. For more information email: jfn@ims.tuwien.ac.at.

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Fundamentals of Ubiquitous Tracking for Augmented Reality

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  1. Fundamentals of Ubiquitous Tracking for Augmented Reality Vienna University of Technology Joe Newman, Thomas Pintaric, Dieter Schmalstieg Technische Universität München Martin Wagner, Asa MacWilliams, Martin Bauer, Gudrun Klinker For more information email: jfn@ims.tuwien.ac.at

  2. Augmented Reality (AR) • Rich and meaningful AR experience • Wearable computing • Graphics rendering,display technologies • Spatial model of world • Up-to-date & coherent • Requires trackers and special sensors • Parallels with UbiComp, but crucial difference • Motivation to look at a global approach to unifying different tracking systems

  3. Ubiquitous Tracking • Graph-based model • Express spatial inter-relationships • Dynamically extendible tracker networks of trackers • High-precision, low latency requirements • Framework extensible: • New trackers, filtering schemes, optimisation criteria

  4. World Model Asdf Asdf Asdf Asdf A sdf

  5. Measured Relationships Asdf Asdf Asdf Asdf Asdf Asdf A S df

  6. Inferred Relationships

  7. Fusion + other filtering “The use of multiple location systems simultaneously to form hierarchical and overlapping levels of sensing to increase accuracy beyond what is possible using any individual system”, Hightower • New inference results in new edge in G(Y) • Model other filtering schemes • Environmental knowledge • Voronoi graphs • Bayesian, particle filters, etc….

  8. Relationship Modelling • Representation • Not just homogeneous matrices • Pixel locations (x,y), 3D positions (x,y,z) • Accelerations and velocities • Attributes • Latency, update frequency, confidence value, pose accuracy, Monetary cost, time-to-live • Difficult to obtain from proprietary devices • Slowly varying (compared to tracking data)

  9. Optical shared tracking • Marker K need only be visible to a single camera • Including other sensors extends range of possible inferences

  10. Future Work • Simulation • Implementation • Studierstube/OpenTracker • DWARF components • Auto-calibration • Model small amounts of uncertainty • Adaptive model • Evaluation

  11. Conclusion • Theoretical framework describing tracking setups • Inter-relationships modelled in a graph • State spaces, attributes, error functions • Extendible • Maps to implementation of UbiTrack concepts (work in progress!) • Partial standardisation of tracker setups • Formal approach towards ubiquity of AR

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