IEICE TRANSACTIONS on Communications
Self-Taught Classifier of Gateways for Hybrid SLAM
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E93-B No.9 pp.2481-2484
- Publication Date
- 2010/09/01
- Publicized
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.E93.B.2481
- Type of Manuscript
- LETTER
- Category
- Navigation, Guidance and Control Systems
Authors
Keyword
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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
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@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},}
Copy
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 -
English
