Self-taught Classifier of Gateways for Hybrid SLAM

Title
Self-taught Classifier of Gateways for Hybrid SLAM
Authors
뉴엔쑤안다오정문호유범재오상록
Keywords
Hybrid SLAM; Self-taught Classifier
Issue Date
2010-09
Publisher
IEICE transactions on communications
Citation
VOL E93-B, NO 9, 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.
URI
http://pubs.kist.re.kr/handle/201004/45594
ISSN
0916-8516
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KIST Publication > Article
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