Maps-based angular PDFs used as prior maps for FootSLAM

FootSLAM (Simultaneous Localization and Mapping) is a new technology that addresses the indoor mapping and positioning challenge that relies only on sensors that the person carries. In this paper, we propose to use maps-based angular Probability Density Functions (PDFs), using prior knowledge of the building layout as prior maps for FootSLAM and show how they can be integrated into the FootSLAM weight update. The angular PDFs - that represent PDFs for a human step direction at a given location - are derived from a diffusion algorithm based on maps. The advantage of using prior maps is that the FootSLAM algorithm reaches convergence faster and more accurately.