LSH-RANSAC: Incremental Matching of Large-Size Maps
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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Kanji TANAKA, Ken-ichi SAEKI, Mamoru MINAMI, Takeshi UEDA, "LSH-RANSAC: Incremental Matching of Large-Size Maps" in IEICE TRANSACTIONS on Information,
vol. E93-D, no. 2, pp. 326-334, February 2010, doi: 10.1587/transinf.E93.D.326.
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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E93.D.326/_p
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@ARTICLE{e93-d_2_326,
author={Kanji TANAKA, Ken-ichi SAEKI, Mamoru MINAMI, Takeshi UEDA, },
journal={IEICE TRANSACTIONS on Information},
title={LSH-RANSAC: Incremental Matching of Large-Size Maps},
year={2010},
volume={E93-D},
number={2},
pages={326-334},
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.},
keywords={},
doi={10.1587/transinf.E93.D.326},
ISSN={1745-1361},
month={February},}
Copy
TY - JOUR
TI - LSH-RANSAC: Incremental Matching of Large-Size Maps
T2 - IEICE TRANSACTIONS on Information
SP - 326
EP - 334
AU - Kanji TANAKA
AU - Ken-ichi SAEKI
AU - Mamoru MINAMI
AU - Takeshi UEDA
PY - 2010
DO - 10.1587/transinf.E93.D.326
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E93-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 2010
AB - 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.
ER -
English
