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Driven to distraction: Self-supervised distractor learning for robust monocular visual odometry in urban environments

Abstract:

We present a self-supervised approach to ignoring “distractors” in camera images for the purposes of robustly estimating vehicle motion in cluttered urban environments. We leverage offline multi-session mapping approaches to automatically generate a per-pixel ephemerality mask and depth map for each input image, which we use to train a deep convolutional network. At run-time we use the predicted ephemerality and depth as an input to a monocular visual odometry (VO) pipeline, using either spar...

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

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Publisher copy:
10.1109/ICRA.2018.8460564

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Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Maddern, W More by this author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Oxford college:
Pembroke College
Publisher:
Institute of Electrical and Electronics Engineers Publisher's website
Publication date:
2018-09-13
Acceptance date:
2018-01-15
DOI:
Pubs id:
pubs:854379
URN:
uri:ebbf9050-a9ea-4e19-a342-c0f708d5a397
UUID:
uuid:ebbf9050-a9ea-4e19-a342-c0f708d5a397
Local pid:
pubs:854379
ISBN:
9781538630815

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