A Semantic Segmentation-based Approach for Train Positioning

Satellite-based positioning has been selected as one of the key game changers for the evolution of the European Rail Traffic Management System, introducing strict accuracy, integrity and continuity requirements. However, GNSSs are vulnerable to several degradations that impair the fulfillment of the performance demands. For this reason, we propose the integration of GNSS and on-board cameras for train positioning. More specifically, we present a positioning framework based on local landmarks which requires semantic segmentation, that is a pixel-wise classification of the input images. In this work, we describe the positioning framework and evaluate the semantic segmentation approach on a publicly available dataset. The achieved results outperform state-of-the-art approaches and, although the semantic segmentation issue in the railway environment has already been addressed, to the best of our knowledge this is the first approach which exploits these techniques for train positioning purposes.