Zhou, Z., Zhao, C., Adolfsson, D. et al. (4 more authors) (2021) NDT-Transformer : large-scale 3D point cloud localisation using the normal distribution transform representation. In: Proceedings of the 2021 IEEE International Conference on Robotics and Automation. ICRA 2021 - IEEE International Conference on Robotics and Automation, 30 May - 05 Jun 2021, Xi’an, China. Institute of Electrical and Electronics Engineers , pp. 5654-5660. ISBN 978-1-7281-9077-8
Abstract
3D point cloud-based place recognition is highly demanded by autonomous driving in GPS-challenged environments and serves as an essential component (i.e. loop-closure detection) in lidar-based SLAM systems. This paper proposes a novel approach, named NDT-Transformer, for real-time and large-scale place recognition using 3D point clouds. Specifically, a 3D Normal Distribution Transform (NDT) representation is employed to condense the raw, dense 3D point cloud as probabilistic distributions (NDT cells) to provide the geometrical shape description. Then a novel NDT-Transformer network learns a global descriptor from a set of 3D NDT cell representations. Benefiting from the NDT representation and NDT-Transformer network, the learned global descriptors are enriched with both geometrical and contextual information. Finally, descriptor retrieval is achieved using a query-database for place recognition. Compared to the state-of-the-art methods, the proposed approach achieves an improvement of 7.52% on average top 1 recall and 2.73% on average top 1% recall on the Oxford Robotcar benchmark.
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. |
Keywords: | Three-dimensional displays; Simultaneous localization and mapping; Shape; Conferences; Transforms; Gaussian distribution; Transformers |
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) |
Funding Information: | Funder Grant number Engineering and Physical Science Research Council EP/R026092/1 The Royal Society RGS\R2\202432 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 22 Mar 2021 13:53 |
Last Modified: | 22 Jun 2023 15:12 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Refereed: | Yes |
Identification Number: | 10.1109/ICRA48506.2021.9560932 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:172335 |