Exploiting Building Information from Publicly Available Maps in Graph-Based SLAM

Olga Vysotska, Cyrill Stachniss

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.