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Existing studies usually employ data driven and learning based models to capture spatial and temporal features for robot location estimation, modeling dynamics of robot and make robot decision. However, the modeling and localization performance is not satisfied. In this paper, to address above challenges, a novel deep learning framework called multi-faceted deep learning based dynamics modeling and robot localization learning (DMLoc) method is proposed. Specifically, a localization attention module is designed to capture the features from original fingerprints and optimized fingerprints information. Then, a multi-faceted localization module is proposed, which integrates extraction model and optimized model with long short-term memory (LSTM) and gate recurrent unit (GRU). Moreover, a multi-feature fusion layer is designed to fuse the extracted features and generate localization results. Extensive simulation results show the efficiency of the proposed DMLoc.<\/jats:p>","DOI":"10.3233\/jifs-230895","type":"journal-article","created":{"date-parts":[[2023,7,14]],"date-time":"2023-07-14T11:39:25Z","timestamp":1689334765000},"page":"5541-5550","source":"Crossref","is-referenced-by-count":1,"title":["Multi-faceted deep learning framework for dynamics modeling and robot localization learning"],"prefix":"10.1177","volume":"45","author":[{"given":"Yuxiang","family":"Shan","sequence":"first","affiliation":[{"name":"China Tobacco Zhejiang Industrial Company Limited"}]},{"given":"Hailiang","family":"Lu","sequence":"additional","affiliation":[{"name":"China Tobacco Zhejiang Industrial Company Limited"}]},{"given":"Weidong","family":"Lou","sequence":"additional","affiliation":[{"name":"China Tobacco Zhejiang Industrial Company Limited"}]}],"member":"179","reference":[{"issue":"99","key":"10.3233\/JIFS-230895_ref1","first-page":"1","article-title":"Recurrent neural networks for accurate rssi indoor localization","volume":"PP","author":"Hoang","year":"2019","journal-title":"IEEE Internet of Things Journal"},{"issue":"99","key":"10.3233\/JIFS-230895_ref2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TKDE.2019.2951103","article-title":"A data-driven sequential localization framework for big telco data","volume":"PP","author":"Zhu","year":"2019","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"issue":"1","key":"10.3233\/JIFS-230895_ref3","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1109\/COMST.2015.2464084","article-title":"Wi-fi fingerprint-based indoor positioning: Recent advances and comparisons","volume":"18","author":"He","year":"2017","journal-title":"IEEE Communications Surveys Tutorials"},{"issue":"9","key":"10.3233\/JIFS-230895_ref4","doi-asserted-by":"crossref","first-page":"16492","DOI":"10.1109\/TITS.2021.3098636","article-title":"A Privacy-Preserving-Based Secure Framework Using Blockchain-Enabled Deep-Learning in Cooperative Intelligent Transport System","volume":"23","author":"Kumar","year":"2022","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"1","key":"10.3233\/JIFS-230895_ref5","first-page":"763","article-title":"CSI-based Fingerprinting forIndoor Localization: A Deep Learning Approach[J]","volume":"66","author":"Wang","year":"2016","journal-title":"IEEE Transactionson Vehicular Technology"},{"key":"10.3233\/JIFS-230895_ref6","doi-asserted-by":"crossref","unstructured":"Chu K. , Lam A. and Li V. , Deep multi-scale convolutional lstm network for travel demand and origin-destination predictions, (2020).","DOI":"10.1109\/TITS.2019.2924971"},{"key":"10.3233\/JIFS-230895_ref7","doi-asserted-by":"crossref","unstructured":"Hsieh H.Y. , Prakosa S.W. and Leu J.S. , Towards the Implementation of Recurrent Neural Network Schemes for WiFi Fingerprint-Based Indoor Positioning, 2018 IEEE 88th Vehicular Technology Conference (VTC-Fall). 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