WiFi based trajectory alignment, calibration and crowdsourced site survey using smart phones and foot-mounted IMUs

Foot-mounted inertial positioning (FMIP) can face problems of inertial drifts and unknown initial states in real applications, which renders the estimated trajectories inaccurate and not obtained in a well defined coordinate system for matching trajectories of different users. In this paper, an approach adopting received signal strength (RSS) measurements for Wifi access points (APs) are proposed to align and calibrate the trajectories estimated from foot mounted inertial measurement units (IMUs). A crowdsourced radio map (RM) can be built subsequently and can be used for fingerprinting based Wifi indoor positioning (FWIP). The foundation of the proposed approach is graph-based simultaneously localization and mapping (SLAM). The nodes in the graph denote users' poses and the edges denote the pairwise constrains between the nodes. The constrains are derived from: (1) inertial estimated trajectories; (2) vicinity in the RSS space. With these constrains, an error functions is defined. By minimizing the error function, the graph is optimized and the aligned/calibrated trajectories along with the RM are acquired. The experimental results have corroborated the effectiveness of the approach for trajectory alignment, calibration as well as RM construction.

I. Introduction

The combination of foot-mounted inertial positioning (FMIP) and fingerprinting based Wifi indoor positioning (FWIP) can have some interesting use cases where none of the single perspective positioning solutions are suitable. For example, at airports or malls, there is a need for both personnel tracking (e.g. security staff) and providing an indoor positioning service for passengers and customers. The latter is preferably achieved by FWIP using the persons' own Wifi enabled mobile devices while requires construction and update of a radio map (RM).