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AEROS: AdaptivE RObust Least-Squares for Graph-Based SLAM

Abstract:

In robot localisation and mapping, outliers are unavoidable when loop-closure measurements are taken into account. A single false-positive loop-closure can have a very negative impact on SLAM problems causing an inferior trajectory to be produced or even for the optimisation to fail entirely. To address this issue, popular existing approaches define a hard switch for each loop-closure constraint. This paper presents AEROS, a novel approach to adaptively solve a robust least squares minimisati...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.3389/frobt.2022.789444

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
ORCID:
0000-0001-6128-7808
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
ORCID:
0000-0003-2940-0879
Publisher:
Frontiers Media Publisher's website
Journal:
Frontiers in Robotics and AI Journal website
Volume:
9
Article number:
789444
Publication date:
2022-04-01
Acceptance date:
2022-01-19
DOI:
EISSN:
2296-9144
Language:
English
Keywords:
Pubs id:
1242862
Local pid:
pubs:1242862
Deposit date:
2022-03-09

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