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

ieee robotics and automation letters

Hough<inline-formula><tex-math notation="LaTeX">$^2$</tex-math></inline-formula>Map &#x2013; Iterative Event-Based Hough Transform for High-Speed Railway Mapping

Hough$^2$Map – Iterative Event-Based Hough Transform for High-Speed Railway Mapping