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.
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Copy
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
Copy
@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 -