Location and vision based Augmented Reality on Android

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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

Thank you!

Questions?

22 simon@bitstars.com