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What Could Move? Finding Cars, Pedestrians and Bicyclists in 3D Laser Data

Abstract:

This paper tackles the problem of segmenting things that could move from 3D laser scans of urban scenes. In particular, we wish to detect instances of classes of interest in autonomous driving applications - cars, pedestrians and bicyclists - amongst significant background clutter. Our aim is to provide the layout of an end-to-end pipeline which, when fed by a raw stream of 3D data, produces distinct groups of points which can be fed to downstream classifiers for categorisation. We postulate ...

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Publication status:
Published

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Publisher copy:
10.1109/ICRA.2012.6224734

Authors


Journal:
2012 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA)
Pages:
4038-4044
Publication date:
2012-01-01
DOI:
ISSN:
1050-4729
URN:
uuid:8b017202-35c4-49ee-a635-f2e12c72cd2a
Source identifiers:
348858
Local pid:
pubs:348858
Language:
English

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