  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 crowd-sourced 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.

Machine Learning;Applications