Hough2Map -- Iterative Event-based Hough Transform for High-Speed Railway Mapping

TL;DR

Hough2Map is an innovative iterative event-based Hough transform framework that enables real-time, accurate mapping of railway infrastructure landmarks like poles using a dynamic vision sensor, enhancing high-speed railway localization.

Contribution

The paper introduces Hough2Map, a novel event-based Hough transform method for real-time detection and mapping of railway poles, addressing challenges like motion blur and lighting variations.

Findings

Detection reliability up to 92%

Mapping accuracy with RMSE of 1.1518 meters

Effective in real-world railway scenarios

Abstract

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.