The impact of sensor errors and building structures on particle filter-based inertial positioning

Positioning systems that do not depend on in-building infrastructures are critical for enabling a range of applications within pervasive computing. Particle filter-based inertial positioning promises infrastructure-less positioning, but previous research has not provided an understanding of how the positioning accuracy of such systems depends on the sensor errors and the building structure. This paper evaluates the impact of sensor errors and building structures on the positioning accuracy using a waist-mounted system named Pro-Position. We analyze results from deploying the system in regular and open spaced office buildings as well as in a shopping mall. The results show that differences in accuracy can be explained by error sources of the sensor and the constraints provided by building structures. Additionally, we present and evaluate methods for using GPS positioning with particle filter-based inertial positioning to improve accuracy in large open areas and to provide seamless handover when entering buildings.