Abstract
One of the unavoidable bottlenecks in the public application of passive signal (e.g., received signal strength, magnetic) fingerprinting-based indoor localization technologies is the extensive human effort that is required to construct and update database for indoor positioning. In this paper, we propose an accurate visual-inertial integrated geo-tagging method that can be used to collect fingerprints and construct the radio map by exploiting the crowdsourced trajectory of smartphone users. By integrating multisource information from the smartphone sensors (e.g., camera, accelerometer, and gyroscope), this system can accurately reconstruct the geometry of trajectories. An algorithm is proposed to estimate the spatial location of trajectories in the reference coordinate system and construct the radio map and geo-tagged image database for indoor positioning. With the help of several initial reference points, this algorithm can be implemented in an unknown indoor environment without any prior knowledge of the floorplan or the initial location of crowdsourced trajectories. The experimental results show that the average calibration error of the fingerprints is 0.67 m. A weighted k-nearest neighbor method (without any optimization) and the image matching method are used to evaluate the performance of constructed multisource database. The average localization error of received signal strength (RSS) based indoor positioning and image based positioning are 3.2 m and 1.2 m, respectively, showing that the quality of the constructed indoor radio map is at the same level as those that were constructed by site surveying. Compared with the traditional site survey based positioning cost, this system can greatly reduce the human labor cost, with the least external information.
Highlights
Nowadays, indoor localization has become a common issue for various location-based services and applications
A number of technologies have been proposed for indoor localization, which are based on different principles, such as Wi-Fi [1], geomagnetic [2], ultra wide band (UWB) [3], ultrasound [4] and so on
Many studies have focused on developing localization scheme that do not rely on extra devices or only use the existing infrastructures, such as Wi-Fi fingerprinting [5,6,7,8], geomagnetic [9], or visual positioning [10,11,12]
Summary
Indoor localization has become a common issue for various location-based services and applications. The collected wireless signal probably will be different for two smartphone devices even at a same spatial location due to the difference of built-in sensors in smartphone It will obviously affect the geo-tagging accuracy of the Wi-Fi or geomagnetic clustering based crowdsourcing methods. This study attempts to develop a visual-based geo-tagging method for crowdsourcing-based indoor localization This method can be used to reconstruct geometrical trajectories (associated with the collected signals) of multiple crowdsourcing users with different. The proposed method can be used to accurately geo-tag the sampling points that were extracted from crowdsourced trajectories and generate different maps or datasets, such as radio map or geo-tagged image database, for indoor positioning.
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