One of the most important problems faced when creating real-time, mobile augmented reality systems is registration error -- the misalignment between the computer generated graphics and the physical world the application is trying to augment.
Such misalignment may either cause the information presented by the application to be misleading to the user or make the augmentation meaningless.
In this work, we question the often implicit assumption that registration error must be eliminated for AR to be useful.
Instead, we take the position that registration error will never
be eliminated and that application developers can build useful AR
applications if they have an estimate of registration error.
We present a novel approach to AR application design:
Spatially Adaptive Augmented Reality (i.e., applications that change their displays based on the quality of the alignment between the physical and virtual world).
The computations used to change the display are based on real-time
estimates of the registration error.
The application developer uses these estimates to build applications that function under a variety of conditions independent of specific tracking technologies.
In order to support Spatially Adaptive AR, this research establishes a theoretical model for AR that accounts for the static uncertainty in the system.
We call this sub set of Spatially Adaptive AR, Uncertainty Aware AR.
The reification of these theoretical contributions is a toolkit that supports the design of Uncertainty Aware AR applications: OSGAR.
This work describes OSGAR in detail and presents examples that demonstrate how to use this novel approach to create adaptable augmentations as well as how to support user interaction in the presence of uncertainty.
Full Reference:
Enylton Machado Coelho. "Spatially Adaptive Augmented Reality" PhD Thesis, Georgia Institute of Technology, 2005
