PipeSLAM: Simultaneous Localisation and Mapping in Feature Sparse Water Pipes using the Rao-Blackwellised Particle Filter
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Ma, K., Schirru, M., Zahraee, A.H., Dwyer-Joyce, R., Boxall, J., DODD, Tony, Collins, R. and Anderson, S.R. (2017) PipeSLAM: Simultaneous Localisation and Mapping in Feature Sparse Water Pipes using the Rao-Blackwellised Particle Filter. In: IEEE International Conference on Advanced Intelligent Mechatronics (AIM), 2017, 3 - 7/7/2017. IEEE, Munich, Germany. ISBN 978-1-5090-5998-0
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eprint6233.pdf - AUTHOR'S ACCEPTED Version (default) Available under License All Rights Reserved. Download (1MB) | Preview |
Abstract or description
Water, a valuable resource, is usually distributed through urban environments by buried pipes. These pipes are difficult to access for inspection, maintenance and repair. This makes in-pipe robots an appealing technology for inspecting water pipes and localising damage prior to repair from above ground. Accurate localisation of damage is of critical importance because of the costs associated with excavating roads, disrupting traffic and disrupting the water supply. The problem is that pipes tend to be relatively featureless making robot localisation a challenging problem. In this paper we propose a novel simultaneous localisation and mapping (SLAM) algorithm for metal water pipes. The approach we take is to excite pipe vibration with a hydrophone (sound induced vibration), which leads to a map of pipe vibration amplitude over space. We then develop a SLAM algorithm that makes use of this new type of map, where the estimation method is based on the Rao-Blackwellised particle filter (RBPF), termed PipeSLAM. The approach is also suited to SLAM in plastic water pipes using a similar type of map derived from ultrasonic sensing. We successfully demonstrate the feasibility of the approach using a combination of experimental and simulation data.
| Item Type: | Book Chapter, Section or Conference Proceeding |
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| Additional Information: | © 2017 IEEE. This is an author produced version of a paper subsequently published in 2017 IEEE International Conference on Advanced Intelligent Mechatronics (AIM). Uploaded in accordance with the publisher's self-archiving policy. |
| Faculty: | School of Creative Arts and Engineering > Engineering |
| Depositing User: | Library STORE team |
| Date Deposited: | 15 Jul 2020 14:43 |
| Last Modified: | 24 Feb 2023 13:58 |
| URI: | https://eprints.staffs.ac.uk/id/eprint/6233 |
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