Geography Reference
In-Depth Information
3.4 Mobile AR Systems
Mobile augmented reality systems represent a challenge due to technological
limitations of any mobile device. Only recently there has been an increase in
mobile AR systems research and development. Even a device that would hold all
the required functionality would quickly run out of battery or be larger than a
handheld device.
Advances in mobile AR development started with the development of an
ARToolkit (Piekarski and Thomas
2002
), which created a way to program mobile
AR systems. Smartphones have since upped the same, with their in-built feature
and functionality, improving at an incredible pace. Every year multiple new
phones are being released. These new advanced features make the smartphone the
obvious choice for augmented reality research and development. There is yet a
solution to be found to the battery life problem, the lifetime of mobile phones is
increasing steadily through advances in hardware and software. It is no longer a
limiting factor. It has to be kept in mind and is an active area of research.
3.4.1 Existing Technologies and Applications
A relevant example of location based AR on mobile phones is an Android
application from a company called Mixare in Italy (Mixare
2011
). This application
gets its information from Wikipedia points of interest and displays them in any
subscribing phone nearby. This library is open source and reuse of its code base is
encouraged.
The StudierstubeES mobile system has ben developed in Austria at the Graz
University of Technology and uses various methods to create the effect of AR
(Schmalstieg et al.
2002
). This library uses natural feature tracking or marker
based tracking to determine where and when the camera is pointing at an object to
be augmented. This library is closed source.
The micro chip manufacturer Qualcomm has also developed a beta version of
its own AR toolkit based on the Studierstube ES technology (Schmalstieg and
Wagner
2008
). It is only available for beta testing and to enable feedback for
completion as an alpha product. This AR toolkit uses the same algorithms as
Studierstube for marker or natural feature detection. It is also closed source but
available without charge.
