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Growing semantically meaningful models for visual SLAM

Abstract:

Though modern Visual Simultaneous Localisation and Mapping (vSLAM) systems are capable of localising robustly and efficiently even in the case of a monocular camera, the maps produced are typically sparse point-clouds that are difficult to interpret and of little use for higher-level reasoning tasks such as scene understanding or human-machine interaction. In this paper we begin to address this deficiency, presenting progress on expanding the competency of visual SLAM systems to build richer ...

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Publication status:
Published

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Publisher copy:
10.1109/CVPR.2010.5540176
Host title:
2010 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)
Pages:
467-474
Publication date:
2010-01-01
DOI:
ISSN:
1063-6919
ISBN:
9781424469840
Pubs id:
pubs:109721
UUID:
uuid:1e981402-b25b-47ae-b237-4b276413c75a
Local pid:
pubs:109721
Source identifiers:
109721
Deposit date:
2012-12-19

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