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.
Highlights
During the last decade, the number of smartphones and mobile devices has increased rapidly, and various location-based applications have arisen, such as location-enabled social networking, navigating, and advertising.As a prerequisite of these location-based applications, outdoor maps are available for almost all regions around the globe
To improve the accuracy of indoor floor plan construction, we propose a novel crowdsourcing indoor floor plan construction algorithm based on magnetic fingerprint
1 depicts the architecture of the crowdsourcing indoor floor plan construction algorithm based on magnetic algorithm consistsindoor of fourfloor keyplan steps: Figure the architectureOur of the crowdsourcing construction algorithm based on magnetic fingerprinting
Summary
The number of smartphones and mobile devices has increased rapidly, and various location-based applications have arisen, such as location-enabled social networking, navigating, and advertising. In recent years, crowdsourcing has been applied to constructing and updating indoor floor plans. In many indoor environments, such anchor points are too sparse to provide sufficient and accurate correction Both over- and underestimation of accessible areas occurs, e.g., when a trace drifts into walls, or when corners exist that users seldom walk around. We propose an improved algorithm for indoor floor plan construction based on magnetic fingerprinting. Our algorithm leverages extensive crowdsourcing data from multiple mobile users to construct the floor plans of indoor environments. Our proposed method combines geomagnetic matching and dead reckoning techniques, and uses trajectory clustering and graphics processing methods to build relatively accurate floor plans.
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