Hough2Map – Iterative Event-Based Hough Transform for High-Speed Railway Mapping
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Date
2021-04
Publication Type
Journal Article
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yes
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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 1.1518 m.11The code is available at https://github.com/ethz-asl/Hough2Map.
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published
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Journal / series
Volume
6 (2)
Pages / Article No.
2745 - 2752
Publisher
IEEE
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Software
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Subject
Computer vision for transportation; object detection; segmentation and categorization; mapping
Organisational unit
02261 - Center for Sustainable Future Mobility / Center for Sustainable Future Mobility