What localizes beneath: A metric multisensor localization and mapping system for autonomous underground mining vehicles
Description
Robustly and accurately localizing vehicles in underground mines is particularly challenging due to the unavailability of GPS, variable and often poor lighting conditions, visual aliasing in long tunnels, and airborne dust and water. In this paper, we present a novel, infrastructure‐less, multisensor localization method for robust autonomous operation within underground mines. The proposed method integrates with existing mine site commissioning and operation procedures and includes both an offline map‐building process and an online localization algorithm. The approach combines the strengths of visual‐based place recognition, LIDAR‐based localization, and odometry in a particle filter fusion process. We provide an extensive experimental validation using new large data sets acquired in two operational Australian underground hard‐rock mines (including a 600m‐deep multilevel mine with approximately 33km of mapping data and 7km of vehicle localization) by actual mining vehicles during production operations. We demonstrate a significant increase in localization accuracy over prior state‐of‐the‐art SLAM research systems and real‐time operation, with processing times in the order of 10 Hz. We present results showing a mean error of 0.68 m from the Queensland Mine data set and 1.32 m from the New South Wales Mine data set and at least 86% reduction in error compared with prior state of the art. We also analyze the impact of the particle filter parameters with respect to localization accuracy. Together this study represents a new approach to positioning systems for currently deployed autonomous vehicles within underground mine environments.
Impact and interest:
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ID Code: | 202878 | ||||||||
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Item Type: | Contribution to Journal (Journal Article) | ||||||||
Refereed: | Yes | ||||||||
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Measurements or Duration: | 23 pages | ||||||||
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DOI: | 10.1002/rob.21978 | ||||||||
ISSN: | 1556-4967 | ||||||||
Pure ID: | 64939470 | ||||||||
Divisions: | Current > Research Centres > Centre for Robotics Current > Research Centres > Centre for Future Mobility/CARRSQ Current > QUT Faculties and Divisions > Faculty of Engineering Current > Schools > School of Electrical Engineering & Robotics Current > QUT Faculties and Divisions > Faculty of Health |
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Copyright Owner: | 2020 Wiley Periodicals LLC | ||||||||
Copyright Statement: | This work is covered by copyright. Unless the document is being made available under a Creative Commons Licence, you must assume that re-use is limited to personal use and that permission from the copyright owner must be obtained for all other uses. If the document is available under a Creative Commons License (or other specified license) then refer to the Licence for details of permitted re-use. It is a condition of access that users recognise and abide by the legal requirements associated with these rights. If you believe that this work infringes copyright please provide details by email to qut.copyright@qut.edu.au | ||||||||
Deposited On: | 04 Aug 2020 04:26 | ||||||||
Last Modified: | 28 Mar 2024 11:01 |
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