Browse Conference Publications > Intelligent Robots and System ... Help Working with Abstracts « Prev | Next » Combined visually and geometrically informative link hypothesis for pose-graph visual SLAM using bag-of-words

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This paper reports on a method to combine expected information gain with visual saliency scores in order to choose geometrically and visually informative loop-closure candidates for pose-graph visual simultaneous localization and mapping (SLAM). Two different bag-of-words saliency metrics are introduced—global saliency and local saliency. Global saliency measures the rarity of an image throughout the entire data set, while local saliency describes the amount of texture richness in an image. The former is important in measuring an overall global saliency map for a given area, and is motivated from inverse document frequency (a measure of rarity) in information retrieval. Local saliency is defined by computing the entropy of the bag-of-words histogram, and is useful to avoid adding visually benign key frames to the map. The two different metrics are presented and experimentally evaluated with indoor and underwater imagery to verify their utility.
Publisher
IEEE/RSJ
Issue Date
2011-09-28
Language
ENG
Citation

Intelligent Robots and Systems (IROS), 2011 IEEE/RSJ International Conference on, pp.1647 - 1654

ISSN
2153-0858
DOI
10.1109/IROS.2011.6094820
URI
http://hdl.handle.net/10203/193788
Appears in Collection
CE-Conference Papers(학술회의논문)
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