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https://doi.org/10.1109/iros.2...
Article . 2017 . Peer-reviewed
Data sources: Crossref
ETH Zürich Research Collection
Conference object . 2017
Data sources: Datacite
DBLP
Conference object
Data sources: DBLP
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Only look once, mining distinctive landmarks from ConvNet for visual place recognition

Authors: Zetao Chen; Fabiola Maffra; Inkyu Sa; Margarita Chli;

Only look once, mining distinctive landmarks from ConvNet for visual place recognition

Abstract

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

ISBN:978-1-5386-2682-5

ISBN:978-1-5386-2681-8

ISBN:978-1-5386-2683-2

Country
Switzerland
Related Organizations
Keywords

Place Recognition, Place Recognition; Convolutional Neural Network; Feature Encoding; Robot Localization, Robot Localization, Convolutional Neural Network, Feature Encoding

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    111
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 1%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 1%
Powered by OpenAIRE graph
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
111
Top 1%
Top 10%
Top 1%
Green