survey

Indoor Localization Improved by Spatial Context—A Survey

Authors:

Milad Ramezani,

Debaditya Acharya,

Kourosh Khoshelham,

Shahrokh Valaee,

Jianga ShangAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 52, Issue 3

Article No.: 64, Pages 1 - 35

https://doi.org/10.1145/3322241

Published: 03 July 2019 Publication History

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 a comprehensive review of state-of-the-art indoor localization methods and localization improvement methods using maps, spatial models, and landmarks.

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Indoor Localization Improved by Spatial Context—A Survey
1. Human-centered computing
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ACM Computing Surveys Volume 52, Issue 3

May 2020

734 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3341324

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Received: 01 September 2018

Published in CSUR Volume 52, Issue 3

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