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Constructing an Indoor Floor Plan Using Crowdsourcing Based on Magnetic Fingerprinting.

Haiyong Luo¹, Fang Zhao², Mengling Jiang³

¹Beijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology Chinese Academy of Sciences, Beijing 100190, China. yhluo@ict.ac.cn.

Sensors (Basel, Switzerland)

|November 22, 2017

Summary

This study introduces an algorithm for automatically creating indoor floor plans and magnetic maps using crowdsourced smartphone data. This method enables accurate indoor mapping without extra hardware, benefiting location-based services.

Keywords:

DTW affinity propagation clustering crowdsourcing floor plan construction indoor localization

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Area of Science:

Computer Science
Geomatics Engineering
Robotics

Background:

Indoor positioning systems require indoor maps, which are often unavailable for most environments.
Manual indoor map construction is slow and limits the adoption of indoor localization systems.

Purpose of the Study:

To propose an algorithm for automatic indoor floor plan and magnetic fingerprint map construction.
To enable widespread utilization of indoor localization systems by overcoming the lack of indoor maps.

Main Methods:

The system uses dead reckoning, a geomagnetic signal observation model, and trajectory fusion via affinity propagation for floor plan construction.
Crowdsourced magnetic trajectory data are clustered using dynamic time warping, and trajectories are inferred from odometry and fused.
The approach leverages smartphone sensors, eliminating the need for additional hardware like laser rangefinders or wheel encoders.

Main Results:

The proposed algorithm successfully constructs indoor floor plans with an accuracy of 0.48 meters.
Trajectory fusion effectively mitigates tracking errors from noisy sensors, yielding highly accurate indoor paths.
The system generates magnetic fingerprint maps concurrently with floor plans.

Conclusions:

The developed algorithm provides an efficient and accurate method for automatic indoor map creation using readily available smartphone data.
This approach significantly enhances the feasibility and scalability of indoor positioning and location-based services in unmapped environments.