Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/717031
Title: Trajectory Estimation and Crowdsourced Radio Map Establishment From Foot-Mounted IMUs, Wi-Fi Fingerprints, and GPS Positions
Other Titles: IEEE Sensors Journal
Authors: Yang Gu|Caifa Zhou|Andreas Wieser|Zhimin Zhou
subject: radio map|Graph-based SLAM|trajectory estimation|indoor positioning|Wi-Fi
Year: -1-Uns- -1
Abstract: Trajectory estimation is a problem derived from a common indoor positioning scenario: the user is perceiving the environment while in motion. This paper focuses on an instance: the dead reckoning data of the user are provided by foot-mounted inertial positioning and the observations in the environment are position estimated from global positioning system (GPS) and Wi-Fi received signal strengths. An approach is proposed to merge the three types of data from multiple users using the graph-based SLAM framework. In constructing the graph, Wi-Fi-based edges are established adaptively and GPS-based edges are established as prior information for the corresponding positions from the dead reckoning generated trajectories. The users' trajectories can be aligned and calibrated using the proposed graph construction strategies. Afterwards, a crowsourced Wi-Fi-based radio map (RM) can be established for other users who need the fingerprinting-based Wi-Fi indoor positioning service. The accuracy of the users' trajectories and the crowdsourced RM are tested to validate the proposed approach.
URI: http://localhost/handle/Hannan/717031
ISBN: 1530-437X
volume: Volume
issue: Issue
Appears in Collections:New Ieee 2019

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Title: Trajectory Estimation and Crowdsourced Radio Map Establishment From Foot-Mounted IMUs, Wi-Fi Fingerprints, and GPS Positions
Other Titles: IEEE Sensors Journal
Authors: Yang Gu|Caifa Zhou|Andreas Wieser|Zhimin Zhou
subject: radio map|Graph-based SLAM|trajectory estimation|indoor positioning|Wi-Fi
Year: -1-Uns- -1
Abstract: Trajectory estimation is a problem derived from a common indoor positioning scenario: the user is perceiving the environment while in motion. This paper focuses on an instance: the dead reckoning data of the user are provided by foot-mounted inertial positioning and the observations in the environment are position estimated from global positioning system (GPS) and Wi-Fi received signal strengths. An approach is proposed to merge the three types of data from multiple users using the graph-based SLAM framework. In constructing the graph, Wi-Fi-based edges are established adaptively and GPS-based edges are established as prior information for the corresponding positions from the dead reckoning generated trajectories. The users' trajectories can be aligned and calibrated using the proposed graph construction strategies. Afterwards, a crowsourced Wi-Fi-based radio map (RM) can be established for other users who need the fingerprinting-based Wi-Fi indoor positioning service. The accuracy of the users' trajectories and the crowdsourced RM are tested to validate the proposed approach.
URI: http://localhost/handle/Hannan/717031
ISBN: 1530-437X
volume: Volume
issue: Issue
Appears in Collections:New Ieee 2019

Files in This Item:
File Description SizeFormat 
08506448.pdf2.26 MBAdobe PDFThumbnail
Preview File
Title: Trajectory Estimation and Crowdsourced Radio Map Establishment From Foot-Mounted IMUs, Wi-Fi Fingerprints, and GPS Positions
Other Titles: IEEE Sensors Journal
Authors: Yang Gu|Caifa Zhou|Andreas Wieser|Zhimin Zhou
subject: radio map|Graph-based SLAM|trajectory estimation|indoor positioning|Wi-Fi
Year: -1-Uns- -1
Abstract: Trajectory estimation is a problem derived from a common indoor positioning scenario: the user is perceiving the environment while in motion. This paper focuses on an instance: the dead reckoning data of the user are provided by foot-mounted inertial positioning and the observations in the environment are position estimated from global positioning system (GPS) and Wi-Fi received signal strengths. An approach is proposed to merge the three types of data from multiple users using the graph-based SLAM framework. In constructing the graph, Wi-Fi-based edges are established adaptively and GPS-based edges are established as prior information for the corresponding positions from the dead reckoning generated trajectories. The users' trajectories can be aligned and calibrated using the proposed graph construction strategies. Afterwards, a crowsourced Wi-Fi-based radio map (RM) can be established for other users who need the fingerprinting-based Wi-Fi indoor positioning service. The accuracy of the users' trajectories and the crowdsourced RM are tested to validate the proposed approach.
URI: http://localhost/handle/Hannan/717031
ISBN: 1530-437X
volume: Volume
issue: Issue
Appears in Collections:New Ieee 2019

Files in This Item:
File Description SizeFormat 
08506448.pdf2.26 MBAdobe PDFThumbnail
Preview File