LSH-RANSAC: Incremental Matching of Large-Size Maps

Kanji TANAKA  Ken-ichi SAEKI  Mamoru MINAMI  Takeshi UEDA  

IEICE TRANSACTIONS on Information and Systems   Vol.E93-D   No.2   pp.326-334
Publication Date: 2010/02/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E93.D.326
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Artificial Intelligence and Cognitive Science
mobile robot,  self-localization,  incremental map-matching,  RANSAC,  LSH,  

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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.