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
Chapter Title
Adapting BIM-Based AR Positioning Techniques to the Construction Site
Authors
Khalid Amin, Grant Mills, Duncan Wilson, Karim Farghaly
DOI
10.36253/979-12-215-0289-3.17
Peer Reviewed
Publication Year
2023
Copyright Information
© 2023 Author(s)
Content License
Metadata License
Book Title
CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality
Book Subtitle
Managing the Digital Transformation of Construction Industry
Editors
Pietro Capone, Vito Getuli, Farzad Pour Rahimian, Nashwan Dawood, Alessandro Bruttini, Tommaso Sorbi
Peer Reviewed
Publication Year
2023
Copyright Information
© 2023 Author(s)
Content License
Metadata License
Publisher Name
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
Series Title
Proceedings e report
Series ISSN
2704-601X
Series E-ISSN
2704-5846