Sun, L. orcid.org/0000-0002-0393-8665, Adolfsson, D., Magnusson, M. et al. (3 more authors) (2020) Localising faster : efficient and precise lidar-based robot localisation in large-scale environments. In: IEEE International Conference on Robotics and Automation (ICRA). IEEE Internation Conference on Robotics and Automation (ICRA) 2020, 31 May - 31 Aug 2020, Virtual. Institute of Electrical and Electronics Engineers , pp. 4386-4392. ISBN 978-1-7281-7395-5
Abstract
This paper proposes a novel approach for global localisation of mobile robots in large-scale environments. Our proposed method leverages learning-based localisation and filtering-based localisation, to localise the robot efficiently and precisely through seeding Monte Carlo Localisation (MCL) with deep learned distribution. In particular, a fast locali-sation system rapidly estimates the 6-DOF pose through a deep-probabilistic model (Gaussian Process Regression with deep kernel), then a precise recursive estimator refines the estimated robot pose according to the geometric alignment. More importantly, the Gaussian method (i.e. deep probabilistic localisation) and non-Gaussian method (i.e. MCL) can be integrated naturally via importance sampling. Consequently, the two systems can be integrated seamlessly and mutually benefit from each other. To verify the proposed framework, we provide a case study in large-scale localisation with a 3D lidar sensor. Our experiments on the Michigan NCLT long-term dataset show that the proposed method is able to localise the robot in 1.94 s on average (median of 0.8 s) with precision of 0.75 m in a large-scale environment of approximately 0.5 km2 .
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 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. |
Keywords: | Robots; Neural networks; Three-dimensional displays; Gaussian processes; Laser radar; Monte Carlo methods; Kernel |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 11 Feb 2020 12:33 |
Last Modified: | 21 Jun 2023 14:08 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Refereed: | Yes |
Identification Number: | 10.1109/ICRA40945.2020.9196708 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:152065 |