Improved heuristic drift elimination with magnetically-aided dominant directions (MiHDE) for pedestrian navigation in complex buildings

The main problem of Pedestrian Dead-Reckoning (PDR) using only a body-attached IMU is the accumulation of heading errors. The heading provided by magnetometers in indoor buildings is in general not reliable and therefore it is commonly not used. Recently, a new method was proposed called Heuristic Drift Elimination (HDE) that minimizes the heading error when navigating in buildings. It assumes that the majority of buildings have their corridors parallel to each other, or they intersect at right angles, and consequently most of the time the person walks along a straight path with a heading constrained to one of four possible directions. In this paper we study the performance of HDE-based methods in complex buildings, i.e. with pathways also oriented at 45°, long curved corridors, and wide areas where non-oriented motion is possible. We explain how the performance of the original HDE method can be deteriorated in complex buildings, and also, how severe errors can appear in case of false matches with the building's dominant directions. Although magnetic compassing indoors has a chaotic behavior, in this paper we analyze large data-sets in order to study the potential use that magnetic compassing has to estimate the absolute yaw angle of a walking person. Apart from these analysis, this paper also proposes an improved HDE method called MiHDE (Magnetically-aided Improved Heuristic Drift Elimination), that is implemented over a PDR framework that uses foot-mounted inertial navigation with an Extended Kalman Filter (EKF). The EKF is fed with the MiHDE-estimated orientation error, gyro bias corrections, as well as the confidence over that corrections. We experimentally evaluated the performance of the proposed MiHDE-based PDR method, comparing it with the original HDE implementation. Results show that both methods perform very well in ideal orthogonal narrow-corridor buildings, and MiHDE outperforms HDE for non-ideal trajectories (e.g. curved paths) and also makes it robust against potential false dominant direction matchings.