Modeling of Structure Landmark for Indoor Pedestrian Localization

The decreasing of accumulative error is a key issue for various multi-sensor fusion-based indoor localization systems that employ pedestrian dead reckoning (PDR) to improve their localization performance. Current studies mainly use activity-based map matching (AMM) to prevent the accumulative error....

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Autores principales:	Tao Liu, Xing Zhang, Qingquan Li, Zhixiang Fang
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2019
Materias:	Landmark indoor structure indoor localization Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/f07e4eaa48ce4d25b05386d28d503f58
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spelling	oai:doaj.org-article:f07e4eaa48ce4d25b05386d28d503f582021-11-19T00:02:29ZModeling of Structure Landmark for Indoor Pedestrian Localization2169-353610.1109/ACCESS.2019.2893935https://doaj.org/article/f07e4eaa48ce4d25b05386d28d503f582019-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/8631021/https://doaj.org/toc/2169-3536The decreasing of accumulative error is a key issue for various multi-sensor fusion-based indoor localization systems that employ pedestrian dead reckoning (PDR) to improve their localization performance. Current studies mainly use activity-based map matching (AMM) to prevent the accumulative error. However, it is vulnerable to mismatch problems, which are usually caused by the randomness of human activities. This paper proposes a structure landmark map matching-based indoor localization approach. Structure landmarks refer to special spatial structures (e.g., intersections, corridors, or corners), which are visually salient in a local environment. These landmarks are visually recognizable in indoor spaces because of their distinct shapes. This paper integrates visual and inertial information to recognize the structure landmarks by using a Bayesian classifier. An algorithm is also proposed to realize indoor localization without prior knowledge of the initial location or the turning angles of people. This approach decreases the accumulative localization error of PDR by matching the detected structure landmarks to the ground-truth values. The experimental results showed that the identification accuracy of the structure landmark was about 90% and the matching accuracy was 92%. The mean off-line localization error was about 1.2 m. Compared with the AMM-based method, this approach is robust to the random turning activities of people and can realize indoor localization with a faster convergence speed.Tao LiuXing ZhangQingquan LiZhixiang FangIEEEarticleLandmarkindoor structureindoor localizationElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 7, Pp 15654-15668 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Landmark indoor structure indoor localization Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Landmark indoor structure indoor localization Electrical engineering. Electronics. Nuclear engineering TK1-9971 Tao Liu Xing Zhang Qingquan Li Zhixiang Fang Modeling of Structure Landmark for Indoor Pedestrian Localization
description	The decreasing of accumulative error is a key issue for various multi-sensor fusion-based indoor localization systems that employ pedestrian dead reckoning (PDR) to improve their localization performance. Current studies mainly use activity-based map matching (AMM) to prevent the accumulative error. However, it is vulnerable to mismatch problems, which are usually caused by the randomness of human activities. This paper proposes a structure landmark map matching-based indoor localization approach. Structure landmarks refer to special spatial structures (e.g., intersections, corridors, or corners), which are visually salient in a local environment. These landmarks are visually recognizable in indoor spaces because of their distinct shapes. This paper integrates visual and inertial information to recognize the structure landmarks by using a Bayesian classifier. An algorithm is also proposed to realize indoor localization without prior knowledge of the initial location or the turning angles of people. This approach decreases the accumulative localization error of PDR by matching the detected structure landmarks to the ground-truth values. The experimental results showed that the identification accuracy of the structure landmark was about 90% and the matching accuracy was 92%. The mean off-line localization error was about 1.2 m. Compared with the AMM-based method, this approach is robust to the random turning activities of people and can realize indoor localization with a faster convergence speed.
format	article
author	Tao Liu Xing Zhang Qingquan Li Zhixiang Fang
author_facet	Tao Liu Xing Zhang Qingquan Li Zhixiang Fang
author_sort	Tao Liu
title	Modeling of Structure Landmark for Indoor Pedestrian Localization
title_short	Modeling of Structure Landmark for Indoor Pedestrian Localization
title_full	Modeling of Structure Landmark for Indoor Pedestrian Localization
title_fullStr	Modeling of Structure Landmark for Indoor Pedestrian Localization
title_full_unstemmed	Modeling of Structure Landmark for Indoor Pedestrian Localization
title_sort	modeling of structure landmark for indoor pedestrian localization
publisher	IEEE
publishDate	2019
url	https://doaj.org/article/f07e4eaa48ce4d25b05386d28d503f58
work_keys_str_mv	AT taoliu modelingofstructurelandmarkforindoorpedestrianlocalization AT xingzhang modelingofstructurelandmarkforindoorpedestrianlocalization AT qingquanli modelingofstructurelandmarkforindoorpedestrianlocalization AT zhixiangfang modelingofstructurelandmarkforindoorpedestrianlocalization
_version_	1718420703724699648

Modeling of Structure Landmark for Indoor Pedestrian Localization

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