This document discusses augmented reality on Android devices. It provides an overview of the DroidAR framework, an open source library for building location-based and marker-based augmented reality apps on Android. The document outlines features of DroidAR versions 1 and 2, such as using the jMonkey game engine and sensor-based tracking. Examples of indoor navigation and surface detection extensions are also presented. The conclusion emphasizes DroidAR's flexibility, ease of use, and suitability for scenarios like games, navigation apps, and multi-user experiences.

1. Location and vision
based Augmented
Reality on Android
Mostafa Akbari & Simon Heinen
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2. 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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3. 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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4. 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 4
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10. More DroidAR v1 Examples
Example videos at 10
youtube.com/user/bitstars/videos
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11. DroidAR v2
JMonkey
Engine
Sensor and
Vision Based
Position
composition
system
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12. 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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13. 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 13
Android app
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14. Sample code
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15. Sample app
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16. Sample app
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17. 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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18. Indoor navigation use case
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19. 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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20. 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 20
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21. Conclusion
High flexibility Easy to use
• Hybrid tracking: Automatic • Reusable components
composition system for positioning • Fast development process
and orientation • No technology lock-in
• Game engine approach • Combinable with our backend
• Targeting high scalability scenarios solutions for location based
applications
Scenarios Customers
• POI Browsers • Tourism
• Games • Education
• Navigation • Industry
• Multi-user/-player scenarios • Culture
• Instruction guides • Game developers
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22. Thank you!
Questions?
simon@bitstars.com 22