Proceedings of the 2022 International Conference on Computer Science, Information Engineering and Digital Economy (CSIEDE 2022)

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Multi-Source and Multi-Dimensional Data Fusion of Magnetic Levitation Track Transportation Based on Digital Twin

Authors
Yuxin Zhang1, Lei Zhang1, *, Guochen Shen1
1Department of Traffic Information and Control Engineering, Tongji University, Shanghai, China
*Corresponding author. Email: reizhg@tongji.edu.cn
Corresponding Author
Lei Zhang
Available Online 30 December 2022.
DOI
10.2991/978-94-6463-108-1_67How to use a DOI?
Keywords
Magnetic Levitation; LIDAR Point Clouds; Digital Twin
Abstract

Digital twin (DT) of large-scale transportation infrastructure plays an important role in the development of intelligent transportation system (ITS), and has become the current research hotspot of ITS. Traditional data fusion has done a lot for intelligent transportation infrastructure. However, it still exists many shortcomings. This paper aims at establishing a multi-source and multidimensional data fusion model of magnetic levitation track based on digital twin. We proposed a data fusion method that can fuse 2D image data and 3D LIDAR point clouds data together, by using Context Capture and Cloud Compare software. This method combines data advantages so that we can optimize the expression of fine particle accuracy. Firstly, we made the aerial triangulation for the image data that was collected with drone, and then reconstructed the dense point clouds and generated the colorful point clouds; next, we fused the colorful point clouds with the LIDAR point clouds data that has been data processed; and finally, we generated the model and accomplished the fusion process of magnetic levitation track model. We compared the digital twin model with the benchmark model from macroscopic to microscopic perspective, the verification results indicated that the error of track flatness is about one centimeter, and the mean distance between the two models is about 0.124 meters, so the digital twin data fusion model fits well.

Copyright
© 2022 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2022 International Conference on Computer Science, Information Engineering and Digital Economy (CSIEDE 2022)
Series
Advances in Computer Science Research
Publication Date
30 December 2022
ISBN
978-94-6463-108-1
ISSN
2352-538X
DOI
10.2991/978-94-6463-108-1_67How to use a DOI?
Copyright
© 2022 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Yuxin Zhang
AU  - Lei Zhang
AU  - Guochen Shen
PY  - 2022
DA  - 2022/12/30
TI  - Multi-Source and Multi-Dimensional Data Fusion of Magnetic Levitation Track Transportation Based on Digital Twin
BT  - Proceedings of the 2022 International Conference on Computer Science, Information Engineering and Digital Economy (CSIEDE 2022)
PB  - Atlantis Press
SP  - 595
EP  - 610
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-108-1_67
DO  - 10.2991/978-94-6463-108-1_67
ID  - Zhang2022
ER  -