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言語種別	英語
発行・発表の年月	2018/12
形態種別	国際会議論文
査読	査読あり
標題	An Episodic Memory Model with Slow Features Extraction for Topological Map Building
執筆形態	共著
掲載誌名	29th 2018 International Symposium on Micro-NanoMechatronics and Human Science (From Micro & Nano Scale Systems to Robotics & Mechatronics Systems)
掲載区分	国外
出版社・発行元	IEEE
巻・号・頁	pp.139-143
総ページ数	5
著者・共著者	Wei Hong Chin, Yuichiro Toda, Naoyuki Kubota, Jinseok Woo, Chu Kiong Loo
概要	This paper presents a new integration model for on-line topological map building with environment slow features detection. The proposed model is formed by the integration of Bayesian Adaptive Resonance Associative Memory (BARAM) and Incremental Slow Feature Analysis (IncSFA). IncSFA incrementally extracts slowly varying features from a rapidly changing input signal. These slow features will be fed to BARAM for environment learning to build a topological map. The explored environment is represented as a set of neurons (nodes) and edges that connecting all nodes. Each neuron represents a distinct place and edges store robot traverse information that leads the robot to travel from one node to another. The proposed model is an unsupervised learning technique that does not require any prior knowledge of what an environment is supposed to be for ease of implementation. The effectiveness of our proposed method is validated by several standardized benchmark datasets.
外部リンクURL	https://ieeexplore.ieee.org/document/8887062