The work evaluates the performance of a custom-built shoe-mounted pedestrian indoor localization system based on commercially available low-cost inertial and magnetic sensors. This self-contained approach employs quaternions for attitude representation, whereas the estimation problem is formulated as a Kalman filter (KF) for INS strapdown mechanization equations. The paper compares three nonlinear KF techniques based on Unscented KF, Extended KF and Error-state Extended KF. Additionally, for poorly observable INS estimates, some magnetic field disturbance compensation mechanisms are implemented and tested for longer walking scenarios. The performance of the developed techniques was tested on a set of both indoor and outdoor paths.
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Ashkar et al. (2013) studied this question.
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