Real-time robot topological localization and mapping with limited visual sampling in simulated buried pipe networks.
| Autor: | Li XS; School of Computing, University of Leeds, Leeds, United Kingdom., Nguyen TL; School of Computing, University of Leeds, Leeds, United Kingdom., Cohn AG; School of Computing, University of Leeds, Leeds, United Kingdom.; Alan Turing Institute, London, United Kingdom.; Tongji University, Shanghai, China.; Shangdong University, Jilin, China., Dogar M; School of Computing, University of Leeds, Leeds, United Kingdom., Cohen N; School of Computing, University of Leeds, Leeds, United Kingdom. |
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| Jazyk: | angličtina |
| Zdroj: | Frontiers in robotics and AI [Front Robot AI] 2023 Nov 23; Vol. 10, pp. 1202568. Date of Electronic Publication: 2023 Nov 23 (Print Publication: 2023). |
| DOI: | 10.3389/frobt.2023.1202568 |
| Abstrakt: | 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. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2023 Li, Nguyen, Cohn, Dogar and Cohen.) |
| Databáze: | MEDLINE |
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