Abstrakt

Despite significant progress in the field of augmented reality (AR), regarding both hardware and software, there is still a lack of universal models and methods that would enable building ubiquitous AR systems that could be used anywhere and anytime, covering different application areas. This dissertation describes a new approach to building AR systems, called the Contextual Augmented Reality Environment (CARE). The CARE approach is based on contextual selection of both the real objects to be augmented and the synthetic augmentation content. Due to the use of context – understood as the location, time, user's preferences and capabilities of the device – the problem of simultaneous tracking of multiple real objects and the problem of applying synthetic augmentation content is significantly reduced. In a given context, only the objects assigned to a given context range, and which are of interest to a user, are tracked and augmented. In the CARE approach, AR presentations are built in real time based on the context by combining resources provided by different entities in a distributed environment. A semantic approach is used to describe the context and the distributed AR environment. The CARE approach consists of four elements: the CARE Architecture of Semantic AR Services, the Semantic Augmented Reality Ontology for modeling CARE environments, the Semantic Discovery and Matching Method for searching and combining AR components in CARE environments, and the Contextual Augmented Reality Language (CARL) for representing contextual AR presentations. In the dissertation, a prototype implementation of the CARE system based on a service-oriented architecture is described and a thorough qualitative and quantitative evaluation is presented. The results of the evaluation indicate that the CARE approach enables efficient modeling of large-scale contextual augmented reality environments.

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