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Fast and robust registration of partially overlapping point clouds
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Arnold, Eduardo, Mozaffari, Sajjad and Dianati, Mehrdad (2022) Fast and robust registration of partially overlapping point clouds. IEEE Robotics and Automation Letters, 7 (2). pp. 1502-1509. doi:10.1109/LRA.2021.3137888 ISSN 2377-3766.
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WRAP-Fast-robust-registration-partially-overlapping-point-clouds-2021 - Accepted Version - Requires a PDF viewer. Download (2528Kb) |
Official URL: https://doi.org/10.1109/LRA.2021.3137888
Abstract
Real-time registration of partially overlapping point clouds has emerging applications in cooperative perception for autonomous vehicles and multi-agent SLAM. The relative translation between point clouds in these applications is higher than in traditional SLAM and odometry applications, which challenges the identification of correspondences and a successful registration. In this paper, we propose a novel registration method for partially overlapping point clouds where correspondences are learned using an efficient point-wise feature encoder, and refined using a graph-based attention network. This attention network exploits geometrical relationships between key points to improve the matching in point clouds with low overlap. At inference time, the relative pose transformation is obtained by robustly fitting the correspondences through sample consensus. The evaluation is performed on the KITTI dataset and a novel synthetic dataset including low-overlapping point clouds with displacements of up to 30m. The proposed method achieves on-par performance with state-of-the-art methods on the KITTI dataset, and outperforms existing methods for low overlapping point clouds. Additionally, the proposed method achieves significantly faster inference times, as low as 410ms, between 5 and 35 times faster than competing methods. Our code and dataset will be available at https://github.com/eduardohenriquearnold/fastreg.
| Item Type: | Journal Article | |||||||||
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| Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TJ Mechanical engineering and machinery T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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| Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science Faculty of Science, Engineering and Medicine > Engineering > Engineering Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) |
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| Library of Congress Subject Headings (LCSH): | Multisensor data fusion , Multisensor data fusion -- Data processing , Multiagent systems, Robotics, Deep learning (Machine learning) , Visual perception -- Data processing, Data sets, Robot vision | |||||||||
| Journal or Publication Title: | IEEE Robotics and Automation Letters | |||||||||
| Publisher: | IEEE | |||||||||
| ISSN: | 2377-3766 | |||||||||
| Official Date: | April 2022 | |||||||||
| Dates: |
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| Volume: | 7 | |||||||||
| Number: | 2 | |||||||||
| Page Range: | pp. 1502-1509 | |||||||||
| DOI: | 10.1109/LRA.2021.3137888 | |||||||||
| Status: | Peer Reviewed | |||||||||
| Publication Status: | Published | |||||||||
| Reuse Statement (publisher, data, author rights): | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other works. | |||||||||
| Access rights to Published version: | Restricted or Subscription Access | |||||||||
| Copyright Holders: | IEEE | |||||||||
| Date of first compliant deposit: | 4 January 2022 | |||||||||
| Date of first compliant Open Access: | 6 January 2022 | |||||||||
| RIOXX Funder/Project Grant: |
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| Open Access Version: |
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