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Location and vision based Augmented Reality on Android 09.04.2013 1 Mostafa Akbari & Simon Heinen
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Transcript of Bitstars
Location and vision based Augmented Reality on Android
09.04.2013
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Mostafa Akbari & Simon Heinen
About us
• Working with AR since 2007
• First version of the DroidAR framework in 2009
• First presentation on the first droidcon
• Founded bitstars in 2012
• Startup out of the university RWTH Aachen
• Augmented reality and location based service infrastructure
• Framework for location based services and crowdsourcing
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Mobile Developer's Guide To The Galaxy • Implementing Augmented Reality
• p.171 AR Usage Scenarios in Mobile
• p.173 Tracking
• p.174 Augmented Reality SDKs
• p.177 AR Developing 101
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DroidAR v1 • Open Source since 2011
• github.com/bitstars/droidar
• Location based and marker based AR
• Project driven development
• 5000 developer worldwide
www.youtube.com/watch?v=brlNkKmw8IY
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More DroidAR v1 Examples
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10 Example videos at youtube.com/user/bitstars/videos
DroidAR v2
JMonkey Engine
Sensor and Vision Based
Position composition
system
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jMonkey Engine • Open source & cross platform
• ~358 years of effort (COCOMO model)
• First commit in June, 2003
• Test the application/game on the development machine using mockups for Android specific components
• SDK based on NetBeans
• New BSD License
• Large community
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Developing your AR app
Create the game/app in jMonkey
Test it on the development machine
Create an Android app and add the DroidAR framework
Add interfaces for Android specific interaction
Use the DroidAR game loader in your Android app
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Sample code
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Sample app
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Sample app
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SLAM
• SLAM = Simultaneous localization and mapping
• No dependencies to environment and no pre-knowledge needed
• Very accurate positioning possible
• Creates 3d point cloud of environment
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Indoor navigation use case
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Possible extensions
• Detecting surfaces
• Positioning objects
• Occlusion and shadow casting of virtual objects
• Barriers for physics simulations
• Object reconstruction
• 3D Object detection
• Matching the recorded features to a pre known 3d object feature set
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Position composition • SLAM
• works well indoor
• Other feature distances in outdoor environments
• Lighting conditions
• Absolute positioning components
• GPS
• WiFi
• Relative positioning components
• SLAM
• Step detection
• Composition system
• Weighting of positioning components
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Conclusion High flexibility
• Hybrid tracking: Automatic composition system for positioning and orientation
• Game engine approach
• Targeting high scalability scenarios
Easy to use
• Reusable components
• Fast development process
• No technology lock-in
• Combinable with our backend solutions for location based applications
Scenarios
• POI Browsers
• Games
• Navigation
• Multi-user/-player scenarios
• Instruction guides
Customers
• Tourism
• Education
• Industry
• Culture
• Game developers 21
