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Predicting alignment risk to prevent localization failure

Abstract:

During localization and mapping the success of point cloud registration can be compromised when there is an absence of geometric features or constraints in corridors or across doorways, or when the volumes scanned only partly overlap, due to occlusions or constrictions between subsequent observations. This work proposes a strategy to predict and prevent laser-based localization failure. Our solution relies on explicit analysis of the point cloud content prior to registration. A model predicti...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/ICRA.2018.8462890

Authors


More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
Publisher:
Institute of Electrical and Electronics Engineers Publisher's website
Publication date:
2018-09-13
Acceptance date:
2018-01-15
DOI:
EISBN:
978-1-5386-3081-5
EISSN:
2577-087X
Pubs id:
pubs:820457
URN:
uri:50969440-84a0-41de-b95d-701bd60f4d3d
UUID:
uuid:50969440-84a0-41de-b95d-701bd60f4d3d
Local pid:
pubs:820457
ISBN:
978-1-5386-3080-8

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