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LSH-RANSAC: Incremental Matching of Large-Size Maps

Kanji TANAKA, Ken-ichi SAEKI, Mamoru MINAMI, Takeshi UEDA

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Summary :

This paper presents a novel approach for robot localization using landmark maps. With recent progress in SLAM researches, it has become crucial for a robot to obtain and use large-size maps that are incrementally built by other mapper robots. Our localization approach successfully works with such incremental and large-size maps. In literature, RANSAC map-matching has been a promising approach for large-size maps. We extend the RANSAC map-matching so as to deal with incremental maps. We combine the incremental RANSAC with an incremental LSH database and develop a hybrid of the position-based and the appearance-based approaches. A series of experiments using radish dataset show promising results.

Publication
IEICE TRANSACTIONS on Information Vol.E93-D No.2 pp.326-334
Publication Date
2010/02/01
Publicized
Online ISSN
1745-1361
DOI
10.1587/transinf.E93.D.326
Type of Manuscript
PAPER
Category
Artificial Intelligence and Cognitive Science

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