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BoxGraph: semantic place recognition and pose estimation from 3D LiDAR

Abstract:

This paper is about extremely robust and lightweight localisation using LiDAR point clouds based on instance segmentation and graph matching. We model 3D point clouds as fully-connected graphs of semantically identified components where each vertex corresponds to an object instance and encodes its shape. Optimal vertex association across graphs allows for full 6-Degree-of-Freedom (DoF) pose estimation and place recognition by measuring similarity. This representation is very concise, condensi...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/IROS47612.2022.9981266

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
ORCID:
0000-0001-6121-5839
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Oxford college:
Keble College
Role:
Author
ORCID:
0000-0001-6562-8454
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Name:
Engineering and Physical Sciences Research Council
Grant:
EP/V000748/1
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Name:
Lloyds Register Foundation
Publisher:
IEEE
Host title:
2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Series:
IROS 2022
Series number:
35
Pages:
7004-7011
Publication date:
2021-12-26
Event title:
2022 IEEE/RSJ International Conference on Intelligent Robots and Systems
Event location:
Kyoto, Japan
Event website:
https://iros2022.org/
Event start date:
2022-10-23
Event end date:
2022-10-27
DOI:
EISSN:
2153-0866
ISSN:
2153-0858
ISBN:
9781665479271
Language:
English
Keywords:
Pubs id:
1325843
Local pid:
pubs:1325843
Deposit date:
2023-02-16

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