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SnapNav: learning mapless visual navigation with sparse directional guidance and visual reference

Abstract:

Learning-based visual navigation still remains a challenging problem in robotics, with two overarching issues: how to transfer the learnt policy to unseen scenarios, and how to deploy the system on real robots. In this paper, we propose a deep neural network based visual navigation system, SnapNav. Unlike map-based navigation or Visual-Teach-andRepeat (VT&R), SnapNav only receives a few snapshots of the environment combined with directional guidance to allow it to execute the navigation task....

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/ICRA40945.2020.9197523

Authors


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Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Sub department:
Computer Science
Role:
Author
ORCID:
0000-0001-8593-2277
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Sub department:
Computer Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Sub department:
Computer Science
Role:
Author
Publisher:
IEEE Publisher's website
Journal:
Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA) Journal website
Publication date:
2020-09-15
Acceptance date:
2020-01-21
Event title:
2020 International Conference on Robotics and Automation
Event location:
Paris, France
Event website:
https://www.icra2020.org/
Event start date:
2020-05-31T00:00:00Z
Event end date:
2020-06-04T00:00:00Z
DOI:
EISBN:
978-1-7281-7395-5
EISSN:
2577-087X
ISSN:
1050-4729
ISBN:
978-1-7281-7396-2
Language:
English
Keywords:
Pubs id:
1089198
Local pid:
pubs:1089198
Deposit date:
2020-02-27

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