Managing Large-Scale Mapping and Localization for Pedestrians using Inertial Sensors

Abstract

Pedestrian navigation in indoor environments without a pre-installed infrastructure still presents many challenges. There are different approaches that address the problem using prior knowledge about the environment when the building plans or similar are available. Since this is not always the case, a family of technologies based on the principle of Simultaneous Localization and Mapping (SLAM) has been proposed. In this paper we will present some estimates on how a mapping process based on FootSLAM - a form of SLAM for pedestrians - might scale for a large-scale collaborative effort eventually encompassing most of our public indoor space, where the mapping entities are humans. Our assumptions on pedestrian motion and area visiting rate together with calculations based on the computational requirements of pedestrian SLAM algorithms allow us to make estimates with regard to the feasibility, scalability and computational cost of wide-scale mapping of indoor areas by pedestrians.