Indoor Positioning based on Bayes Filtering Using Map Information

Outdoor, GNSS enables continuous positioning 24 hours a day, if pseudoranges of at least four satellites are available. For the positioning in indoor environments, such a ubiquitous navigation solution does not exist. Nowadays, typical indoor solutions either depend on special infrastructure or do not fulfill the requirements of an accurate and reliable positioning. Consequently, reliable indoor positioning is based on sensor integration combining as much information as possible. Within this paper, a filter approach for pedestrian positioning is presented, which integrates fingerprinting-based WLAN-positions, motion parameters gained from a pedestrian dead reckoning (PDR) algorithm, barometer-based height information as well as the current activity determined within a pedestrian activity classification (PAC) approach. Geometric constraints, which are derived from a map of the indoor environment, further support the filter in its positioning task.