Magnetic Field Mapping of Railway Lines with Graph SLAM

The earth’s magnetic field along railway tracks is strongly distorted by magnetic material in the vicinity, e.g., steel in rails and reinforced concrete. The resulting magnetic distortions are persistent in time and characteristic for a certain part of the track. Thus, these distortions can be seen as fingerprints that enable localization when a map of the magnetic field is available. This is particularity interesting for areas where global navigation satellite system (GNSS) signals are not available, such as tunnels. Unfortunately, creating the magnetic map in a GNSS-denied area requires a position reference system that is most likely not available. This paper addresses this problem with a graph-based simultaneous localization and mapping (SLAM) algorithm that uses only odometer and magnetometer measurements. The key idea of the proposed algorithm is to use the magnetic field to detect loop closures and to calculate the relative transformation between different nodes in the pose-graph. The algorithm is evaluated based on a data set recorded with the advanced TrainLab of the Deutsche Bahn traveling on a track in Berlin. Results show that the graph SLAM algorithm together with the magnetic loop closure detection reduces and bounds the position error of the odometry.