Exploiting building information from publicly available maps in graph-based SLAM

Abstract

Maps are an important component of most robotic navigation systems and building maps under uncertainty is often referred to as simultaneous localization and mapping or SLAM. Most SLAM approaches start from scratch and build a map only based on their own observations and odometry information. In this paper, we address the problem of how additional information can be exploited, for example from OpenStreetMap. We extend the standard graph-based SLAM formulation by relating the nodes of the pose-graph with an existing map. As this paper suggests, we can relate the newly built maps with information from publicly available maps with the laser range finder data from the robot and in this way improve the map quality. We implemented and evaluated our approach using real world data taken in urban environments. We illustrate that our extension to graph-based SLAM provides better aligned maps and adds only a marginal computational overhead.