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DeepVO: Towards End-to-End Visual Odometry with Deep Recurrent Convolutional Neural Networks

Abstract:

This paper studies monocular visual odometry (VO) problem. Most of existing VO algorithms are developed under a standard pipeline including feature extraction, feature matching, motion estimation, local optimisation, etc. Although some of them have demonstrated superior performance, they usually need to be carefully designed and specifically fine-tuned to work well in different environments. Some prior knowledge is also required to recover an absolute scale for monocular VO. This paper presen...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/ICRA.2017.7989236

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Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
Societies, Other & Subsidiary Companies
Department:
Kellogg College
Oxford college:
Kellogg College
Role:
Author
Publisher:
Institute of Electrical and Electronics Engineers Publisher's website
Journal:
International Conference on Robotics and Automation Journal website
Host title:
ICRA 2017: IEEE International Conference on Robotics and Automation
Publication date:
2017-07-24
Acceptance date:
2017-01-15
DOI:
Source identifiers:
695554
Pubs id:
pubs:695554
UUID:
uuid:6e0ee820-29f3-42ab-bb44-c00363396c4d
Local pid:
pubs:695554
Deposit date:
2017-05-17

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