Attitude Independent Movement Determination using MEMS for Screen-less Pedestrian Navigation

thesis
2013
RWTH Aachen University - Master Thesis · MS Thesis

Abstract

While indoor navigation systems attract more and more attention, smartphone-based dead reckoning systems heavily depend on the user holding the device in aspecific way. As soon as a user puts the device in her trouser pocket or jacket, they fail. In this thesis, we enable smartphones to accurately determine steps and the current bearing of a user even in these conditions.

We build a general model of the walking motion with specific instances for different device locations (trousers pocket, jacket pocket). Slicing the measured data within a step to only extract the segment which provides the most information on the user’s walking direction, our approach has a median absolute error of only 12° (mean: 22°, q75: 25°) on a total of 15 participants completing a total of 49 test runs. Therefore, together with proposed feedback generation methods, this thesis lies the foundation of true hands-free indoor navigation without the need for any infrastructure.

Authors

Jens Helge Reelfs

Artifacts

Topics

Pedestrian Dead Reckoning (PDR) Inertial Navigation Systems (INS) Sensor Fusion for Navigation Step and Heading Detection Big Data in Motion Analysis