Probabilistic landmark based localization of rail vehicles in topological maps

Localization of rail vehicles is fundamental for any autonomous systems to perform tasks in logistics or personal transport. This contribution presents a novel onboard localization system, based on an eddy current sensor system (ECS), that is capable of a precise train localization when combined with a simple topological map. In contrary to commonly applied travel distance determination by integrating the estimated velocity, we propose an event triggered counting approach, which makes use of the unique sensor capabilities. Rail switches are chosen as landmarks for a global map association and as reliable start and end points for the counting procedure. They are extracted via a Bayesian filter approach, in particular hidden Markov models are applied for detection and classification. Additional features are modeled in a subsequent step and merged within a topological map employing a naïve Bayesian approach in the spatial domain. This allows for a flexible sensor integration and an easy determination of the most probable vehicle position based on traveled distanced.