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. 2017 Nov 20;17(11):2678.
doi: 10.3390/s17112678.

Constructing an Indoor Floor Plan Using Crowdsourcing Based on Magnetic Fingerprinting

Haiyong Luo  1 , Fang Zhao  2 , Mengling Jiang  3 , Hao Ma  4 , Yuexia Zhang  5
Affiliations

Constructing an Indoor Floor Plan Using Crowdsourcing Based on Magnetic Fingerprinting

Haiyong Luo et al. Sensors (Basel). .

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.

Keywords: DTW; affinity propagation clustering; crowdsourcing; floor plan construction; indoor localization.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The architecture of the crowdsourcing indoor floor plan construction algorithm based on magnetic fingerprinting.
Figure 2
Figure 2
The magnetic trajectory comparison along the same and different indoor paths: (a) The steady and consistent magnetic sequence along the same path; (b) The different magnetic measurement sequences collected on different paths.
Figure 3
Figure 3
A typical experiment to evaluate the effectiveness of the turn detection method: (a) A long walking trajectory with five turns and six atomic trajectories; (b) The angle accumulation within the pre-defined sliding window corresponding to the trajectory.
Figure 4
Figure 4
The hierarchical clustering framework used for the crowdsourcing atomic trajectories.
Figure 5
Figure 5
The comparison of the step and mean orientations walking along the same atomic trajectory.
Figure 6
Figure 6
The trajectory inference using different heading estimation methods. (a) Trajectory using the heading of each step obtained from the direction average of gyroscope; (b) Trajectory using the average heading of an atomic path obtained from gyroscope as the atomic path heading; (c) Trajectory using the heading of each step obtained from the direction average of compass; (d) Trajectory using the average heading of an atomic path obtained from compass as the atomic path heading.
Figure 6
Figure 6
The trajectory inference using different heading estimation methods. (a) Trajectory using the heading of each step obtained from the direction average of gyroscope; (b) Trajectory using the average heading of an atomic path obtained from gyroscope as the atomic path heading; (c) Trajectory using the heading of each step obtained from the direction average of compass; (d) Trajectory using the average heading of an atomic path obtained from compass as the atomic path heading.
Figure 7
Figure 7
Multiple magnetic sequence match using Dynamic Time Warping (DTW) in the same cluster mapping geomagnetic data into physical location.
Figure 8
Figure 8
Multiple atomic trajectory fusion of trajectories belonging to a cluster.
Figure 9
Figure 9
The layout of the experimental area on the 7th floor of the Institute of Computing Technology, Chinese Academy of Sciences.
Figure 10
Figure 10
The constructed floor plan comparison by using our proposed algorithm and by adopting the step-wise trajectory fusion algorithm.
Figure 11
Figure 11
Floor plan construction error comparison using our proposed algorithm and the step-wise trajectory fusion algorithm.
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