Improved Heuristic Drift Elimination (iHDE) 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. 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. We also propose an improved HDE method called iHDE, 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 iHDE-estimated orientation error, as well as the confidence over that correction. We experimentally evaluated the performance of the proposed iHDE-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 iHDE outperforms HDE for non-ideal trajectories (e.g. curved paths).

[1]  Johann Borenstein,et al.  Heuristic Drift Elimination for Personnel Tracking Systems , 2010, Journal of Navigation.

[2]  Fernando Seco Granja,et al.  Indoor pedestrian navigation using an INS/EKF framework for yaw drift reduction and a foot-mounted IMU , 2010, 2010 7th Workshop on Positioning, Navigation and Communication.

[3]  Chris Hide,et al.  Aiding MEMS IMU with building heading for indoor pedestrian navigation , 2010, 2010 Ubiquitous Positioning Indoor Navigation and Location Based Service.

[4]  Eric Foxlin,et al.  Pedestrian tracking with shoe-mounted inertial sensors , 2005, IEEE Computer Graphics and Applications.

[5]  Fernando Seco Granja,et al.  Simulation of foot-mounted IMU signals for the evaluation of PDR algorithms , 2011, 2011 International Conference on Indoor Positioning and Indoor Navigation.