LSH-RANSAC: Incremental matching of large-size maps

Kanji Tanaka, Ken Ichi Saeki, Mamoru Minami, Takeshi Ueda

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)326-334
Number of pages9
JournalIEICE Transactions on Information and Systems
VolumeE93-D
Issue number2
DOIs
Publication statusPublished - 2010

Keywords

  • Incremental map-matching
  • LSH
  • Mobile robot
  • RANSAC
  • Self-localization

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence

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