Improving Pedestrian Dead Reckoning using Likely and Unlikely Paths

Abstract

Pedestrian Dead Reckoning is a method estimating a persons path from a known starting point based on length and direction of all performed steps. Measuring these parameters, e.g. using inertial sensors, introduces small errors that accumulate quickly into large distance errors. Knowledge of a buildings geography may reduce these errors as it can be used to keep the estimated position from moving through walls and onto likely paths. In this paper, we use building maps to improve localization based on a single foot-mounted inertial sensor. We show how correction algorithms using likely and unlikely paths can rectify errors intrinsic to pedestrian dead reckoning tasks and discuss restrictions and disadvantages of these algorithms. Our quantitative results show an endpoint accuracy improvement of up to 60% when using likely paths and 23% when using unlikely paths. However, both approaches can also decrease accuracy in certain scenarios. We identify those scenarios and offer further ideas for improving Pedestrian Dead Reckoning methods.