Journal article
Indoor Localization Improved by Spatial Context-A Survey
Fuqiang Gu, Xuke Hu, Milad Ramezani, Debaditya Acharya, Kourosh Khoshelham, Shahrokh Valaee, Jianga Shang
ACM Computing Surveys | Association for Computing Machinery | Published : 2019
DOI: 10.1145/3322241
Abstract
Indoor localization is essential for healthcare, security, augmented reality gaming, and many other location-based services. There is currently a wealth of relevant literature on indoor localization. This article focuses on recent advances in indoor localization methods that use spatial context to improve the location estimation. Spatial context in the form of maps and spatial models have been used to improve the localization by constraining location estimates in the navigable parts of indoor environments. Landmarks such as doors and corners, which are also one form of spatial context, have proved useful in assisting indoor localization by correcting the localization error. This survey gives..
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Awarded by National Key Research and Development Program of China
Awarded by CSC
Funding Acknowledgements
This work is jointly supported by the National Key Research and Development Program of China (Grant No. 2016YFB0502200) and the China Scholarship Council-University of Melbourne Research Scholarship (Grant No. CSC 201408420117).