Li, X.S., Nguyen, T.L., Cohn, A.G. et al. (2 more authors) (2023) Real-Time Robot Topological Localization and Mapping with Limited Visual Sampling in Simulated Buried Pipe Networks. Frontiers in Robotics and AI, 10. 1202568. ISSN 2296-9144
Abstract
Introduction: Our work introduces a real-time robotic localization and mapping system for buried pipe networks.
Methods: The system integrates non-vision-based exploration and navigation with an active-vision-based localization and topological mapping algorithm. This algorithm is selectively activated at topologically key locations, such as junctions. Non-vision-based sensors are employed to detect junctions, minimizing the use of visual data and limiting the number of images taken within junctions.
Results: The primary aim is to provide an accurate and efficient mapping of the pipe network while ensuring real-time performance and reduced computational requirements.
Discussion: Simulation results featuring robots with fully autonomous control in a virtual pipe network environment are presented. These simulations effectively demonstrate the feasibility of our approach in principle, offering a practical solution for mapping and localization in buried pipes.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 Li, Nguyen, Cohn, Dogar and Cohen. This is an open-access article distributed under the terms of the Creative Commons Attribution License(CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
Keywords: | pipe networks; topological mapping; localization; autonomous control; robot simulation |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Funding Information: | Funder Grant number EPSRC (Engineering and Physical Sciences Research Council) EP/S016813/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 16 Nov 2023 14:54 |
Last Modified: | 29 Nov 2023 11:44 |
Status: | Published |
Publisher: | Frontiers Media |
Identification Number: | 10.3389/frobt.2023.1202568 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:205331 |