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Mobile AR and Electronic Secretary

Mobile AR and Electronic Secretary. Wouter Pasman. Overview Who am I About UbiCom mobile AR 3. About Cactus electronic secretary. Wouter Pasman 1987-1991: Student Computer Science, Masters completed with honours (Complexity theory, correctness proving, ..)

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Mobile AR and Electronic Secretary

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  1. Mobile AR and Electronic Secretary Wouter Pasman

  2. Overview Who am I About UbiCom mobile AR 3. About Cactus electronic secretary

  3. Wouter Pasman 1987-1991: Student Computer Science, Masters completed with honours (Complexity theory, correctness proving, ..) 1991-1993: Student Musicology, completed first 2 years 1993-1997: Ph.D. Industrial Design Engineering, Delft University. 1997-2001: Post-doc work: UbiCom 2001-2002: Post-doc work: Cactus

  4. One of my hobbies…

  5. UbiCom project Ubiquitous Communications Mobile Augmented Reality (AR) VIDEO: UbiCom short intro

  6. Overview UbiCom structure Outdoor AR Tracking Low latency mobile AR rendering Dynamic simplification Mathematical model for rendering ARC framework QoS, Accuracy curves VRML integration

  7. UbiCom structure Project leader: R.L. Lagendijk (E. Deprettere) P1'wireless' P2 'visual info' P3 'protocols' me Personal supervisor F.W. Jansen Computer Graphics &CAD

  8. P1. Adaptive wireless comm transceivers. Subproject-leader Wouter Serdijn 5 Ph.Ds OFDM codec hardware OFDM on infrared 5 and 17GHz radio link Signal propagation measurements

  9. P2: Audio-visual information processing. Subproject leader: Richard Heusdens 2 postdocs, 4 PhDs Tracking Low-latency rendering Video Coding Visual Presentation Filter Retinal Scanning Display GIS database handling

  10. P3: System architecture and protocols. Subproject leader: Henk Sips 3 postdocs, 2 PhDs ARC QoS system LART mobile sys Voltage Scaling P123 interface Java compiler

  11. VIDEO: Outdoor AR Tracking VIDEO: Low latency mobile AR rendering

  12. Dynamic Simplification Dynamic LoD generation in backbone Maximize perf/cost ratio in headset.

  13. Mathematical model per object • Estimate link and CPU load, memory usage, lifetime of objects, etc • Est screenspace error and geometric distortions D=0.001 R=1m

  14. QoS: Scheduling of resources Funkhouser,Séquin (1992), Mason,Blake (1997) Goal: maximize benefit within a cost budget.

  15. Mason showed this is NP complete.. - Iterative approx giving in worst case half the maximum possible quality - Quality only known after iteration - Only feedback loop with application possible

  16. QoS Management UbiCom-wide QoS mechanism: ARC Phase I At top level, acceptable options for operation of the server are specified or changed.

  17. Phase IIa Server searches for possibilities, considering • internal state • ARC services A number of options within the requested range are returned.

  18. Phase IIb An optimizer filters the best options and returns them to the client

  19. Phase III At top level the operation point is chosen and a contract is established. The server probably needs to set its own contracts accordingly.

  20. Accuracy Curves To fit graphics rendering to ARC, each node in scene graph is seen as a 'client' in ARC. Each is assigned an accuracy curve • required resources as function of accuracy target • monotonically increasing. R->#polygons

  21. Measurement of geometric distortion d as function of number of polygons n d~ C/n. Accuracy a =1/d Resource usage r = K a + R0 -> piecewise linear function

  22. Propagating accuracy curves Leaf nodes: accuracy curve from (1) mathematical model or (2) measurements Other nodes: propagate curve upwards through scenegraph

  23. LoD node behaviour Resource Usage

  24. Different end points curve 2 Resource usage incorrect extension curve 1 incorrect minimum 20 50 Accuracy

  25. Complexity Root client/node in scene graph: Target Accuracy -> Resources required Idem R -> A (so not NP-complete)

  26. Optimizing curve updates Upto now: screenspace error=visual accuracy Refresh required if user moves Optimizations: Determine range where a curve is ‘accurate enough’ as long as viewer is within the range. (2) Visual accuracy is derived from geometric distortion - which is viewpoint independent geometric accuracy object radius Relative acc =

  27. Using relative accuracy curves Slight changes in algorithms: • grouping -> 'object' diameter changes. • Conversion to visual accuracy needed. Group's bbox is much closer to viewer than the individual bboxes -> convert to visual accuracy at reasonable distance.

  28. VRML integration Accuracy curves & simplified objects: valid only in part of space. Efficient checking with ConicRange. d

  29. Imposter nodes Replace children with image. Automatic refresh & calc of accuracy curve SimpleImposter { MFNode children [ ] SFVec2f size 2 2 SFBool autosize true SFVec3f center 0 0 0 SFBool autocenter true }

  30. LOD LOD picks valid level requiring least resources

  31. Statue on the campus • Prototype implementation of all previous • Very complex, implementation was simplified at several places (caching, prediction, etc)

  32. Statue application Tracker was not yet working -> tracker stub

  33. DEMO VIDEO

  34. Cactus project Context Aware Communication, Terminal and User

  35. Overview Scenario sketch Cactus structure, manpower First ideas for architecture

  36. Cactus Scenario: cultural outing

  37. Current electronic secretary (PCA) Existing PCA's: • enhancing comm between customer and business representative • 'Unified Messaging': voice,email,fax • often also agenda and conference booking • Menu-like voice interface KPN Eileen • Additional: news, weather, teletekst, tv programs, travel info, etc • Human operator

  38. Cactus electronic secretary Adding context sensitivity Tracking the user and estimating user plans Pro-active secretary

  39. Project structure 1 of 13 projects of Freeband Kennisimpuls project (www . freeband.nl)(EZ and OCW) Cactus consists of 2 phases 2002-2004: Cactus Impulse 2004-2006: Cactus

  40. Cactus Impulse structure Project leader: R.L. Lagendijk UseT TermiNet me Personal supervisor F.W. Jansen Computer Graphics &CAD

  41. UseT (User & Terminal) Three subprojects: Trust, Consistency and Interaction (1 Ph.D.) User-context Analysis, Modeling and Sensing (1 Ph.D.) i-DEA Proof-of-Concept (me + 1 Ph.D.)

  42. Questions UseT.1 "Trust" Will the user see the system as consistent and trust it? Which cognitive and user interface factors are most important wrt trust and consistency? eg, when should system ask and not ask? how should information and questions be presented? Wizard of Oz studies to test our system and ideas

  43. Questions UseT.2 "Sensing" Which context- and personalization parameters are relevant for Eileen? How can these parameters be sensed? How can this info best be stored?

  44. Questions T.3 "Proof of Concept" What is proper architecture for UseT System? What reasoning system is appropriate? What filtering of information based on user preferences and context is possible?

  45. Knowledge required • Knowledge of applications • Knowledge of user • Knowledge on inferring user plans, goals etc • Action planning system • Knowledge about relatives of user • Etc..

  46. Architecture sketch

  47. Appropriate Reasoning system In our case, subtile differences have large effects, but not so many causes -> classical AI (Minsky)

  48. Proposal: logical goal- and plan-inference In style of Wilensky's Plan Application Mechanism

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