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ConSLAM: Periodically Collected Real-World Construction Dataset for SLAM and Progress Monitoring

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Computer Vision – ECCV 2022 Workshops (ECCV 2022)

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Abstract

Hand-held scanners are progressively adopted to workflows on construction sites. Yet, they suffer from accuracy problems, preventing them from deployment for demanding use cases. In this paper, we present a real-world dataset collected periodically on a construction site to measure the accuracy of SLAM algorithms that mobile scanners utilize. The dataset contains time-synchronised and spatially registered images and LiDAR scans, inertial data and professional ground-truth scans. To the best of our knowledge, this is the first publicly available dataset which reflects the periodic need of scanning construction sites with the aim of accurate progress monitoring using a hand-held scanner.

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Notes

  1. 1.

    http://www.cvlibs.net/datasets/kitti.

  2. 2.

    Please consult www.hesaitech.com.

  3. 3.

    https://www.blender.org/.

  4. 4.

    https://hilti-challenge.com/dataset-2021.html.

  5. 5.

    https://hilti-challenge.com/dataset-2022.html.

  6. 6.

    https://leica-geosystems.com/products/laser-scanners/scanners/leica-rtc360.

  7. 7.

    https://github.com/ros-drivers/velodyne.

  8. 8.

    see https://github.com/chennuo0125-HIT/lidar_imu_calib, and https://blog.csdn.net/weixin_37835423/article/details/110672571.

  9. 9.

    https://www.ros.org.

  10. 10.

    https://wiki.ros.org/message_filters/ApproximateTime.

  11. 11.

    We used the distance-based downsampling implemented in CloudCompare 2.12.2. See https://www.cloudcompare.org.

  12. 12.

    https://github.com/HKUST-Aerial-Robotics/A-LOAM.

  13. 13.

    https://wiki.ros.org/rosbag.

  14. 14.

    https://opencv.org.

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Acknowledgment

The authors would like to thank Laing O’Rourke for allowing access to their construction site and for collecting the ground truth scans. We also acknowledge Romain Carriquiry-Borchiari of Ubisoft France for his help with rendering some of the figures. This work is supported by the EU Horizon 2020 BIM2TWIN: Optimal Construction Management & Production Control project under an agreement No. 958398. The first author would also like to thank BP, GeoSLAM, Laing O’Rourke, Topcon and Trimble for sponsoring his studentship funding.

Author information

Authors and Affiliations

  1. Department of Engineering, University of Cambridge, Cambridge, UK

    Maciej Trzeciak, Yasmin Fathy & Ioannis Brilakis

  2. Inria Sophia Antipolis-Méditerranée, Valbonne, France

    Kacper Pluta & Pierre Alliez

  3. Laing O’Rourke, Dartford, DA2 6SN, UK

    Lucio Alcalde, Stanley Chee & Antony Bromley

Authors
  1. Maciej Trzeciak
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  2. Kacper Pluta
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Corresponding author

Correspondence to Maciej Trzeciak .

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Editors and Affiliations

  1. IBM Research - MIT-IBM Watson AI Lab, Massachusetts, USA

    Leonid Karlinsky

  2. Technion – Israel Institute of Technology, Haifa, Israel

    Tomer Michaeli

  3. Kyoto University, Kyoto, Japan

    Ko Nishino

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Trzeciak, M. et al. (2023). ConSLAM: Periodically Collected Real-World Construction Dataset for SLAM and Progress Monitoring. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds) Computer Vision – ECCV 2022 Workshops. ECCV 2022. Lecture Notes in Computer Science, vol 13807. Springer, Cham. https://doi.org/10.1007/978-3-031-25082-8_21

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  • DOI: https://doi.org/10.1007/978-3-031-25082-8_21

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