Integrating Known Locations in FootSLAM and Investigating the Influence of Different Prior Information

Abstract

Pedestrian positioning without receiving any GNSS signal or other reference signals as it might be the case in indoor environments or tunnels is still a challenging field of interest. In infrastructure-free approaches that are considered also for professional applications Inertial Measurement Units (IMUs) mounted on the foot or other parts of the body are commonly used. IMU based techniques still suffer from the remaining drift especially when the environment is unknown and not re-visited, or when the pedestrian walks randomly in large areas. In order to overcome this problem the so called FootSLAM algorithm, that is applied in this paper and performs already very good in buildings with restricted room sizes assuming re-visiting the area, is extended to handle known locations. FootSLAM is a SLAM (Simultaneous Localization and Mapping) algorithm estimating the map while walking wearing an inertial sensor on the foot or at other locations of the body. With the use of few known locations, the estimated FootSLAM map can be refined and re-used again for other users walking in that specific area. Beside the derivation of the algorithm handling known locations in FootSLAM, the influence of different kinds of prior knowledges on the FootSLAM algorithm is analyzed in this paper in terms of error and map quality performance.