Heirich, Oliver (DLR German Aerospace Center), Robertson, Patrick (DLR), Cardalda García, Adrián (DLR), Strang, Thomas (German Aerospace Center (DLR)), Lehner, Andreas (German Aerospace Center (DLR))

Probabilistic Localization Method for Trains

Scheduled for presentation during the Poster Session "Poster session III" (TuPO3S), Tuesday, June 5, 2012, 09:50−11:20, Room T1

2012 Intelligent Vehicles Symposium, June 3-7, 2012, Alcalá de Henares, Spain

This information is tentative and subject to change. Compiled on October 19, 2019

Abstract

The localization of trains in a railway network is necessary for train control or applications such as autonomous train driving or collision avoidance systems. Train localization is safety critical and therefore the approach requires a robust, precise and track selective localization. Satellite navigation systems (GNSS) might be a candidate for this task, but measurement errors and the lack of availability in parts of the railway environment do not fulfill the demands for a safety critical system. Therefore, additional onboard sensors, such as an inertial measurement unit (IMU), odometer and railway feature classification sensors (e.g. camera) are proposed. In this paper we present a top-down train localization approach from theory. We analyze causal dependencies and derive a general Bayesian filter. Furthermore we present a generic algorithm based on particle filter in order to process the multi-sensor data, the train motion and a known track map. The particle filter estimates a topological position directly in the track map without using map matching techniques. First simulations with simplified particular state and measurement models show encouraging results in critical railway scenarios.