Research Article
Mobile Augmented Reality Guides in Cultural Heritage
@INPROCEEDINGS{10.4108/eai.30-11-2016.2266954, author={Panagiotis Galatis and Damianos Gavalas and Vlasios Kasapakis and Grammati Pantziou and Christos Zaroliagis}, title={Mobile Augmented Reality Guides in Cultural Heritage}, proceedings={The 8th EAI International Conference on Mobile Computing, Applications and Services}, publisher={ACM}, proceedings_a={MOBICASE}, year={2016}, month={12}, keywords={augmented reality mobile guide cultural heritage archaeological site knossos occlusion user evaluation}, doi={10.4108/eai.30-11-2016.2266954} }
- Panagiotis Galatis
Damianos Gavalas
Vlasios Kasapakis
Grammati Pantziou
Christos Zaroliagis
Year: 2016
Mobile Augmented Reality Guides in Cultural Heritage
MOBICASE
ACM
DOI: 10.4108/eai.30-11-2016.2266954
Abstract
Mobile augmented reality (MAR) technology creates unprecedented possibilities for delivering engaging, immersive experiences to the visitors of cultural heritage sites. Despite the proliferation of available prototypes, the relevant literature still lacks studies investigating the way that users interact with MAR interfaces as well as identifying major usability problems and technology acceptance factors. Herein, we present KnossosAR, a MAR guide implemented for the archaeological site of Knossos (in Crete, Greece) which serves as a testbed for pursuing the abovementioned research objectives while also comparing the (dis)advantages of MAR vs. map-based mobile interfaces in outdoor cultural heritage sites. Among other technical contributions, KnossosAR addresses the occlusion problem, which is commonly encountered in location-based AR applications; that is, it employs an efficient method for estimating the field of view (FoV) of the user in order to handle situations wherein a point of interest is occluded by a physical obstacle (e.g. building). We have conducted field trials which provide preliminary evidence of the efficiency, effectiveness and utility of KnossosAR (including the incorporated FoV estimation approach).