Pedestrian indoor Localization and Tracking using a Particle Filter combined with a learning Accessibility Map (Bachelorarbeit)


Julian Straub


As mobile phones are starting to get equipped with inertial sensors, indoor navigation for pedestrians becomes an increasingly interesting topic in research. This work aims to develop and evaluate the use of a Particle Filter to deal with noisy sensor measurements of an Inertial Measurement Unit (IMU) providing localization and tracking of a pedestrian in indoor environments. Designed at the Institute for Real-Time Computer Systems (RCS), the so called Inav-System was used, which can extract the motion of a person from inertial sensor measurements. On this basis a Particle Filter was implemented, which uses Dead Reckoning in combination with a geometric floor plan to localize and track a person wea- ring the Inav-System in a building. In addition the concept of the Accessibility Map (AM) is proposed which reflects human walking preferences in the degree of accessibility of space in a floor and which makes it possible to exploit this information in the designed Particle Filter. Reinterpreting the AM as a Radial Basis Function Network, a special type of Neural Network, a method for learning accessibility of space in a floor is derived. Measurements show that the additional use of the AM in the Particle Filter yields an improvement in the localization accu- racy of up to 32%, resulting in an average accuracy of up to 1.1m. Deploying the AM and the learning AM, also a more robust tracking could be observed.This means besides the ability to monitor the walking patterns of a pedestrian in a building with a Particle Filter, the localization accuracy and the tracing robustness could be enhanced by the proposed AM.