Conference item icon

Conference item

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...

Expand abstract
Publication status:
Published
Peer review status:
Peer reviewed
Version:
Accepted manuscript

Actions


Access Document


Files:
Publisher copy:
10.1109/ICRA.2017.7989236

Authors


More by this author
Institution:
University of Oxford
Department:
Oxford, MPLS, Computer Science
Role:
Author
More by this author
Institution:
University of Oxford
Department:
Oxford, MPLS, Computer Science
Role:
Author
More by this author
Institution:
University of Oxford
Department:
Oxford, MPLS, Computer Science
Role:
Author
More by this author
Institution:
University of Oxford
Department:
Kellogg College
Role:
Author
Publisher:
Institute of Electrical and Electronics Engineers Publisher's website
Publication date:
2017-07-24
Acceptance date:
2017-01-15
DOI:
Pubs id:
pubs:695554
URN:
uri:6e0ee820-29f3-42ab-bb44-c00363396c4d
UUID:
uuid:6e0ee820-29f3-42ab-bb44-c00363396c4d
Local pid:
pubs:695554

Terms of use


Metrics


Views and Downloads






If you are the owner of this record, you can report an update to it here: Report update to this record

TO TOP