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 논문에서는 하이브리드 SLAM을 위한 자가 학습 게이트웨이 분류기를 제안합니다. 게이트웨이는 SVM 분류기인 자가 학습 분류기에 의해 감지되고 인식되며 사용자 개입 없이 훈련 샘플이 생성되고 레이블이 지정된다는 점에서 자가 학습됩니다. 획득된 메트릭 맵의 토폴로지 경계에서 게이트웨이를 감지하면 스펙트럼 클러스터링 방법을 사용하는 이전 하이브리드 SLAM 접근 방식과 비교하여 메트릭 맵을 서브 맵으로 분할할 때 계산 복잡성이 줄어들기 때문입니다. O(2n)에 O(n), 어디서 n 서브맵의 개수입니다. 이를 통해 대규모 미터법 지도에서도 실시간 하이브리드 SLAM이 가능해집니다. 우리는 다양한 실험을 통해 자가 학습 분류기가 하이브리드 SLAM에서 만족스러운 일관성과 계산 효율성을 제공한다는 것을 확인했습니다.
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.
부
Xuan-Dao NGUYEN, Mun-Ho JEONG, Bum-Jae YOU, Sang-Rok OH, "Self-Taught Classifier of Gateways for Hybrid SLAM" in IEICE TRANSACTIONS on Communications,
vol. E93-B, no. 9, pp. 2481-2484, September 2010, doi: 10.1587/transcom.E93.B.2481.
Abstract: This paper proposes a self-taught classifier of gateways for hybrid SLAM. Gateways are detected and recognized by the self-taught classifier, which is a SVM classifier and self-taught in that its training samples are produced and labeled without user's intervention. Since the detection of gateways at the topological boundaries of an acquired metric map reduces computational complexity in partitioning the metric map into sub-maps as compared with previous hybrid SLAM approaches using spectral clustering methods, from O(2n) to O(n), where n is the number of sub-maps. This makes possible real time hybrid SLAM even for large-scale metric maps. We have confirmed that the self-taught classifier provides satisfactory consistency and computationally efficiency in hybrid SLAM through different experiments.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.E93.B.2481/_p
부
@ARTICLE{e93-b_9_2481,
author={Xuan-Dao NGUYEN, Mun-Ho JEONG, Bum-Jae YOU, Sang-Rok OH, },
journal={IEICE TRANSACTIONS on Communications},
title={Self-Taught Classifier of Gateways for Hybrid SLAM},
year={2010},
volume={E93-B},
number={9},
pages={2481-2484},
abstract={This paper proposes a self-taught classifier of gateways for hybrid SLAM. Gateways are detected and recognized by the self-taught classifier, which is a SVM classifier and self-taught in that its training samples are produced and labeled without user's intervention. Since the detection of gateways at the topological boundaries of an acquired metric map reduces computational complexity in partitioning the metric map into sub-maps as compared with previous hybrid SLAM approaches using spectral clustering methods, from O(2n) to O(n), where n is the number of sub-maps. This makes possible real time hybrid SLAM even for large-scale metric maps. We have confirmed that the self-taught classifier provides satisfactory consistency and computationally efficiency in hybrid SLAM through different experiments.},
keywords={},
doi={10.1587/transcom.E93.B.2481},
ISSN={1745-1345},
month={September},}
부
TY - JOUR
TI - Self-Taught Classifier of Gateways for Hybrid SLAM
T2 - IEICE TRANSACTIONS on Communications
SP - 2481
EP - 2484
AU - Xuan-Dao NGUYEN
AU - Mun-Ho JEONG
AU - Bum-Jae YOU
AU - Sang-Rok OH
PY - 2010
DO - 10.1587/transcom.E93.B.2481
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E93-B
IS - 9
JA - IEICE TRANSACTIONS on Communications
Y1 - September 2010
AB - This paper proposes a self-taught classifier of gateways for hybrid SLAM. Gateways are detected and recognized by the self-taught classifier, which is a SVM classifier and self-taught in that its training samples are produced and labeled without user's intervention. Since the detection of gateways at the topological boundaries of an acquired metric map reduces computational complexity in partitioning the metric map into sub-maps as compared with previous hybrid SLAM approaches using spectral clustering methods, from O(2n) to O(n), where n is the number of sub-maps. This makes possible real time hybrid SLAM even for large-scale metric maps. We have confirmed that the self-taught classifier provides satisfactory consistency and computationally efficiency in hybrid SLAM through different experiments.
ER -