Online calibrated, energy-aware and heading corrected pedestrian navigation with foot-mounted MARG sensors

The objective of this paper is to propose an energy-aware and gait corrected pedestrian dead reckoning (PDR) approach using foot-mounted magnetic, angular rate and gravity (MARG) sensors. Compared with existing algorithms of PDR, the proposed method aims to solve three main problems for real pedestrian applications. First, to avoid limitations of off-line calibration for personal step length parameters, we utilize the zero-velocity-update (ZUPT) aided pedestrian MARG performance to continuously compute one’s pose information. Meanwhile, it accumulates the moving distance for further estimation of one’s step length during the initialization process. Secondly, due to different pedestrian gaits implicating the heading deviation angle between one’s moving direction and heading, there are non-negligible impacts on pedestrian dead reckoning accuracy. The linear Kalman filter (KF) is used to recursively estimate the deviated heading during aforementioned initialization process. The third problem is related to the energy consumption. Following three aspects of adaptive energy saving work are devised: (i) energy-aware strategy for gyroscopes measurements acquisition is adopted to guarantee lower energy consumption. (ii) Modes switching mechanism of navigation computation is applied to the initialization and dead reckoning processes. (iii) De-sampling after initialization process has been invoked. Finally, real-world experiments are carried out to evaluate the performances of developed PDR system. The results show the efficiency of the suggested approach.