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Sensors 2017, 17(11), 2678; https://doi.org/10.3390/s17112678

Constructing an Indoor Floor Plan Using Crowdsourcing Based on Magnetic Fingerprinting

1
Beijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology Chinese Academy of Sciences, Beijing 100190, China
2
School of Software Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
3
School of Information and Telecommunication Engineering, Beijing Information Science and Technology University, Beijing 100101, China
*
Author to whom correspondence should be addressed.
Received: 31 October 2017 / Revised: 18 November 2017 / Accepted: 19 November 2017 / Published: 20 November 2017
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Abstract

A large number of indoor positioning systems have recently been developed to cater for various location-based services. Indoor maps are a prerequisite of such indoor positioning systems; however, indoor maps are currently non-existent for most indoor environments. Construction of an indoor map by external experts excludes quick deployment and prevents widespread utilization of indoor localization systems. Here, we propose an algorithm for the automatic construction of an indoor floor plan, together with a magnetic fingerprint map of unmapped buildings using crowdsourced smartphone data. For floor plan construction, our system combines the use of dead reckoning technology, an observation model with geomagnetic signals, and trajectory fusion based on an affinity propagation algorithm. To obtain the indoor paths, the magnetic trajectory data obtained through crowdsourcing were first clustered using dynamic time warping similarity criteria. The trajectories were inferred from odometry tracing, and those belonging to the same cluster in the magnetic trajectory domain were then fused. Fusing these data effectively eliminates the inherent tracking errors originating from noisy sensors; as a result, we obtained highly accurate indoor paths. One advantage of our system is that no additional hardware such as a laser rangefinder or wheel encoder is required. Experimental results demonstrate that our proposed algorithm successfully constructs indoor floor plans with 0.48 m accuracy, which could benefit location-based services which lack indoor maps. View Full-Text
Keywords: indoor localization; floor plan construction; crowdsourcing; affinity propagation clustering; DTW indoor localization; floor plan construction; crowdsourcing; affinity propagation clustering; DTW
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Luo, H.; Zhao, F.; Jiang, M.; Ma, H.; Zhang, Y. Constructing an Indoor Floor Plan Using Crowdsourcing Based on Magnetic Fingerprinting. Sensors 2017, 17, 2678.

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