Abstract
Fingerprinting-based Wi-Fi indoor positioning has great potential for positioning in GPS-denied areas. However, establishing a fingerprinting map (also called a radio map) prior to positioning (site survey) is normally a labor-intensive task. This paper proposes a method for easy site survey without need for any extra hardware. The user can conduct the site survey adopting only a smart phone. The collected inertial-based readings are processed using the pedestrian dead-reckoning algorithms to generate a raw trajectory. Then a factor graph optimization method is proposed to re-estimate the trajectory by adding constraints originated from collected Wi-Fi fingerprints and landmark positions. The proposed method is verified through an experiment in a mall. The mean positioning error is 1.10 m and the maximum error is 2.25 m. This level of positioning accuracy is considered sufficient for radio map generation purposes. A classical baseline algorithm, the k-Nearest Neighbor (kNN) algorithm, is adopted to test the positioning performance of the radio map (RM), which also validates the quality of the constructed RM from the proposed method.
Highlights
Positioning in GPS-denied areas has been attracting great attention for decades and currently there are many solutions for that
The users only need to carry Wi-Fi-enabled devices, such as smart phones, to locate themselves in indoor environments with abundant Wi-Fi signals. This feature renders the solution suitable for the consumer indoor positioning market as either Wi-Fi-enabled devices or Wi-Fi signals are prevalent in many indoor environments, such as malls and airports; (2) The positioning error is within limited bounds
If two poses are connected with a green line, it means the fingerprints collected at the two poses are with vicinity in the signal space (Wi-Fi distance less than a threshold)
Summary
Positioning in GPS-denied areas has been attracting great attention for decades and currently there are many solutions for that. Fingerprinting-based Wi-Fi positioning is one of these solutions with great potential for the following two reasons: (1) No extra hardware is needed. The users only need to carry Wi-Fi-enabled devices, such as smart phones, to locate themselves in indoor environments with abundant Wi-Fi signals. This feature renders the solution suitable for the consumer indoor positioning market as either Wi-Fi-enabled devices or Wi-Fi signals are prevalent in many indoor environments, such as malls and airports; (2) The positioning error is within limited bounds. Unlike inertial-based indoor positioning methods, such as [1,2], the positioning error will not accumulate with time.
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