Evans, M.H., Aitken, J.M. orcid.org/0000-0003-4204-4020 and Anderson, S.R. orcid.org/0000-0002-7452-5681 (2022) Assessing the feasibility of monocular visual simultaneous localization and mapping for live sewer pipes : a field robotics study. In: 2021 20th International Conference on Advanced Robotics (ICAR). 20th International Conference on Advanced Robotics (ICAR), 06-10 Dec 2021, Virtual conference. Institute of Electrical and Electronics Engineers , pp. 1073-1078. ISBN 9781665436854
Abstract
Sewer pipes are important to inspect for damage and blockages. Mobile robots with cameras are a natural choice for inspecting sewers, and indeed CCTV inspection using tethered mobile platforms is a well-established commercial approach. It therefore makes sense to also explore the use of camera data for localising defects for targeting subsequent repair. Visual odometry (VO) methods have been researched for robot localisation in pipes but the full visual simultaneous localisation and mapping (vSLAM) problem has received little attention. Whilst VO focuses on estimating the current pose of the robot, vSLAM focuses on building a map, as well as pose estimation, which should increase accuracy and robustness - both important for the future use of autonomous robots in sewer inspection. In particular, it is not known if one crucial element of vSLAM - loop closing using appearance-recognition methods - works effectively in sewer pipes due to problems of perceptual aliasing - where the high degree of visual similarity in image frames can lead to incorrect loop closures causing the vSLAM system to fail. The aim of this paper is to assess the feasibility of vSLAM for sewer pipes using real world data. The results demonstrate that whilst perceptual aliasing is a problem, appearance-recognition using bag-of-words methods can be used effectively. Demonstrating for the first time that vSLAM systems are potentially useful for sewer pipe environments.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
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 Sciences Research Council EP/S016813/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 06 Jul 2022 11:27 |
Last Modified: | 08 Jul 2022 07:19 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Refereed: | Yes |
Identification Number: | 10.1109/ICAR53236.2021.9659486 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:188730 |