  To cope with the growing demand for transportation on the railway system,
accurate, robust, and high-frequency positioning is required to enable a safe
and efficient utilization of the existing railway infrastructure. As a basis
for a localization system we propose a complete on-board mapping pipeline able
to map robust meaningful landmarks, such as poles from power lines, in the
vicinity of the vehicle. Such poles are good candidates for reliable and long
term landmarks even through difficult weather conditions or seasonal changes.
To address the challenges of motion blur and illumination changes in railway
scenarios we employ a Dynamic Vision Sensor, a novel event-based camera. Using
a sideways oriented on-board camera, poles appear as vertical lines. To map
such lines in a real-time event stream, we introduce Hough2Map, a novel
consecutive iterative event-based Hough transform framework capable of
detecting, tracking, and triangulating close-by structures. We demonstrate the
mapping reliability and accuracy of Hough2Map on real-world data in typical
usage scenarios and evaluate using surveyed infrastructure ground truth maps.
Hough2Map achieves a detection reliability of up to 92% and a mapping root mean
square error accuracy of 1.1518m.
