ConSLAM: Construction Data Set for SLAM
Publication: Journal of Computing in Civil Engineering
Volume 37, Issue 3
Abstract
This paper presents a data set collected periodically on a construction site. The data set aims to evaluate the performance of simultaneous localization and mapping (SLAM) algorithms used by mobile scanners or autonomous robots. It includes ground-truth scans of a construction site collected using a terrestrial laser scanner along with five sequences of spatially registered and time-synchronized images, lidar scans, and inertial data coming from our prototypical handheld scanner. We also recover the ground-truth trajectory of the mobile scanner by registering the sequential lidar scans to the ground-truth scans and show how to use a popular software package to measure the accuracy of SLAM algorithms against our trajectory automatically. To the best of our knowledge, this is the first publicly accessible data set consisting of periodically collected sequential data on a construction site.
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Data Availability Statement
The whole data set described in this paper is accessible under the following link: https://github.com/mac137/ConSLAM.
Acknowledgments
The authors would like to thank Laing O’Rourke for allowing access to their construction site and collecting the ground-truth scans. We also acknowledge Romain Carriquiry-Borchiari of Ubisoft France for his help in rendering some of the figures and Amanda Xu, a summer intern at the University of Cambridge, who helped us with anonymizing the images. This work is also supported by the EU Horizon 2020 BIM2TWIN: Optimal Construction Management & Production Control project under Agreement No. 958398. The first author would also like to thank BP, GeoSLAM, Laing O’Rourke, Topcon, and Trimble for sponsoring his studentship funding.
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Information & Authors
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Published In
Journal of Computing in Civil Engineering
Volume 37 • Issue 3 • May 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Oct 7, 2022
Accepted: Jan 11, 2023
Published online: Mar 11, 2023
Published in print: May 1, 2023
Discussion open until: Aug 11, 2023
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