While Building Information Modelling (BIM) can support the management and visualisation of construction projects, Augmented Reality (AR) holds great promise to enhance interaction with these complex models. The accurate positioning of BIM-AR models in construction sites is critical to ensure that the virtual and real-world environments are correctly aligned. Through a literature review, this paper presents a review of state-of-the-art positioning techniques. It explores the different techniques used to position BIM-AR models and understands the interconnections and differences between them, with an emphasis on their applicability to the construction industry. The review also explores the challenges and limitations of each technique, in terms of the trade-offs between accuracy, computational efficiency, and robustness in varying environments. By providing an overview of positioning techniques in BIM-AR, this paper aims to guide researchers and practitioners in assessing the suitability of these techniques in the context of construction sites. The insights gained from this review may inform the development of efficient BIM-AR platforms that are more aligned with the dynamic and complex nature of construction sites
University College London, United Kingdom - ORCID: 0000-0002-9705-5280
University College London, United Kingdom - ORCID: 0000-0002-3792-7227
University College London, United Kingdom - ORCID: 0000-0001-6041-8044
University College London, United Kingdom - ORCID: 0000-0003-0717-7434
Titolo del capitolo
Adapting BIM-Based AR Positioning Techniques to the Construction Site
Autori
Khalid Amin, Grant Mills, Duncan Wilson, Karim Farghaly
DOI
10.36253/979-12-215-0289-3.17
Opera sottoposta a peer review
Anno di pubblicazione
2023
Copyright
© 2023 Author(s)
Licenza d'uso
Licenza dei metadati
Titolo del libro
CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality
Sottotitolo del libro
Managing the Digital Transformation of Construction Industry
Curatori
Pietro Capone, Vito Getuli, Farzad Pour Rahimian, Nashwan Dawood, Alessandro Bruttini, Tommaso Sorbi
Opera sottoposta a peer review
Anno di pubblicazione
2023
Copyright
© 2023 Author(s)
Licenza d'uso
Licenza dei metadati
Editore
Firenze University Press
DOI
10.36253/979-12-215-0289-3
eISBN (pdf)
979-12-215-0289-3
eISBN (xml)
979-12-215-0257-2
Collana
Proceedings e report
ISSN della collana
2704-601X
e-ISSN della collana
2704-5846