学位论文详细信息
Spatially Adaptive Augmented Reality
Computer graphics;Registration error;Visualization;Human-computer interaction;Uncertainty
Coelho, Enylton Machado ; Computing
University:Georgia Institute of Technology
Department:Computing
关键词: Computer graphics;    Registration error;    Visualization;    Human-computer interaction;    Uncertainty;   
Others  :  https://smartech.gatech.edu/bitstream/1853/7583/1/coelho_enylton_m_200512_phd.pdf
美国|英语
来源: SMARTech Repository
PDF
【 摘 要 】

One of the most important problems in real-time, mobile augmented reality 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 implied 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 addition to introducing Spatially Adaptive AR, this research establishes a theoretical model for AR. These theoretical contributions are manifested in a toolkit that supports the design of Spatially Adaptive 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.

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