What are the AR delivery considerations?
From Scarlet Wiki
- To maximise student benefit, tablet devices were seen to “offer increased potential for AR applications since they feature larger screens (typically 7-10” compared to the 3-4” screens offered by most smartphones)“.
- The launch of the Apple iPad2 with rear facing camera meant that augmented user experiences could be enhanced immeasurably, particularly if these devices could be available in designated study areas for both casual and directed use. AR software could then be preloaded and bundled user support made available.
- It is important to maximise user adoption by providing access via different handset/tablet OS if possible. Presenting the AR experience on a cross platform App such as Junaio or Layar allows greater inclusivity than developing for a single platform.
- Many AR browsers require smartphones/tablets with fast processors and sufficiently high memory to operate effectively.
- MP3 players running Apple iOS such as later generation iPods can be used to deliver AR, provided they are connected to WiFi networks.
- Using AR apps and downloading media can be battery intensive so it is important that devices are charged on a regular basis
- Due to the environmental constraints of the technology being used inside, the traditional format of augmented delivery – POI’s (Points of Interest) mapped to GPS co-ordinates was problematic. Mobile devices struggle to detect accurate location-based data with their inbuilt GPS, or in some cases will not work at all due to compass interference.
- Remember to optimize content depending on the level of network or WiFi coverage available. Users become frustrated when content is not immediately displayed so carefully consider the types of media used.
- When creating image recognition based AR, it is important to consider environmental conditions such as lighting. Although many visual search algorithms are now very accurate, reflective surfaces, changeable lighting and shadows can have a detrimental impact on their effectiveness. Avoid reference images that are flat and blurry, too dark/bright. Instead use images with little symmetry and focus on high contrast areas that the device camera can uniquely identify.