Aitken, J. orcid.org/0000-0003-4204-4020, Evans, M., Worley, R. orcid.org/0000-0002-3607-2650 et al. (5 more authors) (2021) Simultaneous localization and mapping for inspection robots in water and sewer pipe networks: a review. IEEE Access, 9. pp. 140173-140198. ISSN 2169-3536
Abstract
At the present time, water and sewer pipe networks are predominantly inspected manually. In the near future, smart cities will perform intelligent autonomous monitoring of buried pipe networks, using teams of small robots. These robots, equipped with all necessary computational facilities and sensors (optical, acoustic, inertial, thermal, pressure and others) will be able to inspect pipes whilst navigating, selflocalising and communicating information about the pipe condition and faults such as leaks or blockages to human operators for monitoring and decision support. The predominantly manual inspection of pipe networks will be replaced with teams of autonomous inspection robots that can operate for long periods of time over a large spatial scale. Reliable autonomous navigation and reporting of faults at this scale requires effective localization and mapping, which is the estimation of the robot’s position and its surrounding environment. This survey presents an overview of state-of-the-art works on robot simultaneous localization and mapping (SLAM) with a focus on water and sewer pipe networks. It considers various aspects of the SLAM problem in pipes, from the motivation, to the water industry requirements, modern SLAM methods, map-types and sensors suited to pipes. Future challenges such as robustness for long term robot operation in pipes are discussed, including how making use of prior knowledge, e.g. geographic information systems (GIS) can be used to build map estimates, and improve the multi-robot SLAM in the pipe environment
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ |
Keywords: | Water; Sewer; Network; Pipe Networks; Robots; SLAM; Data Fusion; Bayesian Estimation; Visual Odometry; Laser and Lidar Scanning |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Funding Information: | Funder Grant number ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL EP/S016813/1 Engineering and Physical Sciences Research Council 2135757 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 29 Sep 2021 10:27 |
Last Modified: | 26 Oct 2021 16:13 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Refereed: | Yes |
Identification Number: | 10.1109/ACCESS.2021.3115981 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:178604 |