Karnezis, A., Worley, R., Blight, A. et al. (3 more authors) (2025) Feature detection and classification in buried pipes using LiDAR technology. In: Proceedings of the The 21st International Computing & Control in the Water Industry Conference,, CCWI 2025. The 21st International Computing & Control in the Water Industry Conference,, CCWI 2025, 01-03 Sep 2025, Sheffield, UK. University of Sheffield.
Abstract
Buried infrastructure presents unique challenges for autonomous robotic inspection due to its confined geometry and the structural variations within pipe networks. While CCTV is widely used for pipe inspection, LiDAR sensors offer complementary advantages, including precise ranging capabilities and accurate depth perception. In this work, we introduce a low-cost LiDAR-based system designed to detect blockages and accurately identify critical structural features - such as joints, manholes, and other discontinuities - within these environments in real-time. By combining robust data acquisition, efficient processing, and clear decision-making criteria, the approach enhances the effectiveness, reliability, and automation of underground inspections.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2025 The Author(s). This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
Keywords: | LiDAR; Feature detection; Decision making |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > School of Electrical and Electronic Engineering |
Funding Information: | Funder Grant number EUROPEAN COMMISSION - HORIZON EUROPE 101189847 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 14 Jul 2025 14:45 |
Last Modified: | 09 Sep 2025 11:21 |
Status: | Published |
Publisher: | University of Sheffield |
Refereed: | Yes |
Identification Number: | 10.15131/shef.data.29920931.v1 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:229154 |