Enhanced Precision in Built Environment Measurement: Integrating AprilTags Detection with Machine Learning

Enhanced Precision in Built Environment Measurement: Integrating AprilTags Detection with Machine Learning Shengtao Tan, Aravind Srinivasaragavan, Kepa Iturralde, Christoph Holst Pages 1295-1298 (2024 Proceedings of the 41st ISARC, Lille, France, ISBN 978-0-6458322-1-1, ISSN 2413-5844) Abstract: In the field of building renovation with prefabricated modules, accurately locating and identifying connectors' positions and orientations is an essential technological challenge. For building renovation with prefabricated modules, traditional methods like total stations are not only time-consuming but also highly dependent on experienced technicians. However, previous research has proven that ApriTtag tags can be effectively used in building measurements. This paper proposes a refined AprilTag detection pipeline that integrates machine learning techniques, significantly improving detection accuracy. Moreover, this process can be easily used by non-experts making it more accessible and less time-consuming. Keywords: AprilTag, Machine Learning, Neural Network, Building Measurement DOI: https://doi.org/10.22260/ISARC2024/0167 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley