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Using learning of speed to stabilize scale in monocular localization and mapping

Abstract:

Monocular visual localization and mapping algorithms are able to estimate the environment only up to scale, a degree of freedom which leads to scale drift, difficulty closing loops, and eventual failure. This paper describes an imagedriven approach for scale-drift correction which uses a convolutional neural network to infer the speed of the camera from successive monocular video frames. We obtain continuous drift correction, avoiding the need for explicit higherlevel representations of the m...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/3DV.2017.00066

Authors


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Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Oxford college:
St Anne's College
Role:
Author
Huawei Technologies Co. Ltd. More from this funder
Publisher:
Institute of Electrical and Electronics Engineers Publisher's website
Journal:
5th International Conference on 3D Vision (3DV 2017) Journal website
Host title:
International Conference on 3D Vision 2017
Publication date:
2018-06-07
Acceptance date:
2017-09-03
DOI:
Source identifiers:
817254
Pubs id:
pubs:817254
UUID:
uuid:121f206b-5ddb-4b02-aa41-6ab44c7bfab5
Local pid:
pubs:817254
Deposit date:
2018-01-09

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