Zhang, R., Worley, R., Edwards, S. et al. (3 more authors) (2023) Visual simultaneous localisation and mapping for sewer pipe networks leveraging cylindrical regularity. IEEE Robotics and Automation Letters, 8 (6). pp. 3406-3413. ISSN 2377-3766
Abstract
This work proposes a novel visual Simultaneous Localisation and Mapping (vSLAM) approach for robots in sewer pipe networks. One problem of vSLAM in pipes is that the scale drifts and accuracy degrades. We propose the use of structural information to mitigate this problem via cylindrical regularity. The main novelty consists of an approach for cylinder detection that is more robust than previous methods in non-smooth sewer pipe environments. Cylindrical regularity is then incorporated into both local bundle adjustment and pose graph optimisation. The approach adopts a minimal cylinder representation with only five parameters, avoiding constraints during the optimisation in vSLAM. A further novelty is that the estimated cylinder is part of the scale drift estimation, which enables a correction to the translation estimate and this further improves the accuracy. The approach, termed Cylindrical Regularity ORB-SLAM (CRORB), is benchmarked and compared to leading visual SLAM algorithms ORB-SLAM2 and direct sparse odometry (DSO), as well as a vSLAM algorithm with cylindrical regularity developed for gas pipes, using real sewer pipe data and synthetic data generated with the Gazebo modelling software. The results demonstrate that CRORB improves substantially over the competitors, with a reduction of approximately 70% in error on real data.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 The Authors. Except as otherwise noted, this author-accepted version of a journal article published in IEEE Robotics and Automation Letters is made available via the University of Sheffield Research Publications and Copyright Policy under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ |
Keywords: | Optimization; Point cloud compression; Simultaneous localization and mapping; Visualization; Robots; Principal component analysis; Matrix converters |
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 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 18 Apr 2023 09:38 |
Last Modified: | 02 Oct 2024 11:07 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Refereed: | Yes |
Identification Number: | 10.1109/LRA.2023.3268013 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:198087 |
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Licence: CC-BY 4.0