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

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.