A comparison of Pedestrian Dead-Reckoning algorithms using a low-cost MEMS IMU

TLDR

Human localization is valuable, but conventional LPS require complex infrastructure; IMUs offer a simpler alternative via pedestrian dead‑reckoning, though MEMS sensors face accuracy and drift challenges. The study employs low‑performance MEMS foot‑mounted sensors to describe, implement, and compare algorithms for step detection, stride length, heading, and position estimation. The methodology uses a foot‑mounted MEMS IMU comprising triaxial accelerometers, gyroscopes, and magnetometers to capture motion data. Tests outdoors and indoors showed stride‑length errors of ~1% and positioning errors below 5% of total distance, with absolute orientation estimation being the main error source.

Abstract

Human localization is a very valuable information for smart environments. state-of-the-art local positioning systems (LPS) require a complex sensor-network infrastructure to locate with enough accuracy and coverage. Alternatively, inertial measuring units (IMU) can be used to estimate the movement of a person, by detecting steps, estimating stride lengths and the directions of motion; a methodology that is called pedestrian dead-reckoning (PDR). In this paper, we use low-performance microelectromechanical (MEMS) inertial sensors attached to the foot of a person. This sensor has triaxial orthogonal accelerometers, gyroscopes and magnetometers. We describe, implement and compare several of the most relevant algorithms for step detection, stride length, heading and position estimation. The challenge using MEMS is to provide location estimations with enough accuracy and a limited drift. Several tests were conducted outdoors and indoors, and we found that the stride length estimation errors were about 1%. The positioning errors were almost always below 5% of the total travelled distance. The main source of positioning errors are the absolute orientation estimation.