The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
본 논문은 랜드마크 맵을 이용한 로봇 위치 파악을 위한 새로운 접근 방식을 제시합니다. 최근 SLAM 연구가 진전되면서 로봇이 다른 매퍼 로봇에 의해 점진적으로 구축된 대형 지도를 획득하고 사용하는 것이 중요해졌습니다. 우리의 현지화 접근 방식은 이러한 증분 및 대형 지도에서 성공적으로 작동합니다. 문헌에서 RANSAC 지도 일치는 대형 지도에 대한 유망한 접근 방식이었습니다. 증분 맵을 처리하기 위해 RANSAC 맵 매칭을 확장합니다. 우리는 증분 RANSAC를 증분 LSH 데이터베이스와 결합하고 위치 기반 접근 방식과 모양 기반 접근 방식의 하이브리드를 개발합니다. 무 데이터세트를 사용한 일련의 실험은 유망한 결과를 보여줍니다.
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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
부
@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},}
부
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 -