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Capturing Reality Changes from Point Clouds for Updating Road Geometric Digital Twins

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Reja, Varun Kumar 
Brilakis, Ioannis 


Proactive maintenance of roads enables increased asset lifespan with improved safety and minimised downtime. However, the absence of up-to-date structured information leads to expensive reactive maintenance. Geometric digital twins (GDT) provide digital replicas of object geometry, but no automatic tools exist to maintain them. This paper develops a method for detecting and applying geometric changes to road GDTs. Our solution performs a distance-based comparison of point clouds, estimates the changes and then applies them to GDT. It achieves a 77.87 F1-score in detecting changes and significantly saves time by automating the process, making such digital twins viable and practically applicable.



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2024 European Conference on Computing in Construction

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This work was supported by the UK Engineering and Physical Sciences Research Council (EPSRC) Industrial CASE in partnership with National Highways and Costain [grant No EP/V056441/1] and the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie [grant agreement No 101034337]