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osgAR: a Scene Graph with Uncertain Transformations

osgAR: a Scene Graph with Uncertain Transformations. Enylton Machado Coelho Blair MacIntyre Augmented Environments Lab, GVU - CoC Simon Julier Naval Research Lab. Topics. What is AR? Registration Error Scene graphs & osgAR Components Limitations Current & future work. Augmented Reality.

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osgAR: a Scene Graph with Uncertain Transformations

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  1. osgAR: a Scene Graph with Uncertain Transformations Enylton Machado CoelhoBlair MacIntyre Augmented Environments Lab, GVU - CoC Simon Julier Naval Research Lab

  2. Topics • What is AR? • Registration Error • Scene graphs & osgAR • Components • Limitations • Current & future work

  3. Augmented Reality • Augment, not replace, the physical world with computer-generated objects

  4. AR in Maintenance • Microvision Honda trial • Access to maintenance library Reference www.microvision.com/hondatrial

  5. AR Using Visually Coupled Head-worn Displays • Combine graphics with physical world

  6. Registration Error • Misalignment between the computer generated graphics and the physical object

  7. Registration Error • Commonly used approach • Better trackers • More accurate modeling and calibration • Faster computers • Not practical in real situations • Trackers may break • Knowledge will never be complete

  8. Registration Error • Our approach • Assume errors will always exist • Estimate resulting registration errors • Use error estimates to drive the graphics • Developers concentrate on the intent of the augmentations • Decouple from tracker characteristics

  9. Registration Error • Changing what is being displayed ameliorates the registration error LABELS

  10. Registration Error • Once the registration error can be estimated, different augmentation techniques can be tested • Estimating the error at run time is the hard part Reference www.microvision.com/hondatrial

  11. Topics • What is AR? • Registration Error • Scene graphs & osgAR • Components • Limitations • Current & future work

  12. Scene Graphs • Rigid transformations • Hierarchical representation • Widely adopted • Inventor, Java3D,…

  13. Scene Graphs with Uncertainty • Error estimates are propagated down the graph

  14. Previous Work:Statistical Error Estimation • Individual vertices • 2D screen region Reference VR’02 –Estimating and Adapting to Registration Errors in AR Systems

  15. osgAR:Architecture • Based on OpenSceneGraph (www.openscenegraph.org) • Extended to Augmented Reality • Support for AR • Uncertainty Reference ISMAR’04 –osgAR: A Scene Graph with Uncertain Transformations

  16. osgAR:AR Support • Video in the background • Tracker support • VRPN • ARToolkit • 2D interface manager

  17. osgAR:Computing the Estimate • Model the Uncertainty as a Gaussian • Adds a covariance matrix to the original 4x4 matrix transformation

  18. Bounding Regions • Inner: Always inside the object • Outer: Contains the object BOUNDER

  19. osgAR:Exposing the Estimate • Region: polygonal representation of the regions • Assessment: single value corresponding to the object’s registration error

  20. osgAR:Examples of Using the Estimates • Region • Label Placer • Bounder • Assessment • LOE CALLOUTS Reference ISAR’00 –Adapting to Registration Errors Using Level of Error (LOE) Filtering

  21. Multiple Trackers:Transformation Combiner • Multiple paths to a transform • Callback function picks which to use • Parameter: list of error estimates • Return: which path and estimate to use

  22. Multiple Trackers:Transformation Combiner COMBINER Base Sensor Camera COMB+BOUNDER

  23. osgAR:Architecture • AR Support • Estimate • Computation • Expose • Examples • Multiple Trackers

  24. Observations • Should use shortest path in graph • Camera tracker • Hack: reset error at camera • Head/object tracked with same sensor • Solution: more elaborate bookkeeping/traversal • Leverage redundant information

  25. Camera Uncertainty attached to the world attached to the camera

  26. Pending Transforms • Transformations other then tracker transformations are updated by the system PENDING

  27. Current and Future Work • Generic model that computes the optimal registration error estimate • Exploit the redundancy in the system • Possibility of adding interaction • Applicability and limitations of current computer graphics models

  28. Acknowledgements • Members of the AEL and GVU for many discussions and ideas • ONR grant N000140010361 FOR MORE INFO... www.cc.gatech.edu/ael

  29. Error Estimation • Compute statistical properties for each vertex of an object • Aggregate these estimates per object

  30. Statistical Error Estimation(Simon Julier, NRL) • Unscented Transformation • Easy to implement • More accurate than linearization • Fast

  31. Error Estimate Aggregation • 2D Convex Hull • Project error bounds on 2D screen • Compute convex hull

  32. osgAR: Traversals • Optimizer • 3D uncertainty propagation • Registration error computation

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