Csongor Horvath
Research Scientist
Biography
Csongor Horvath holds a BSc and MSc in Mechatronics Engineering from the Budapest University of Technology and Economics, Hungary. In 2015, he joined the Industrial PhD program at the same university in collaboration with a Norwegian company in robotics, funded by the Research Council of Norway. His doctoral research focused on the development of industrial robotic applications in challenging environments, utilizing machine learning. He joined NIBIO as a Post-Doctor in 2020 and is currently employed as a Research Scientist. His work intersects mechatronics, robotics, and forest operations, where he actively develops prototype devices. These devices integrate emerging technologies into data collection processes within forest operations and silvicultural practices, leveraging technological innovation to enhance operational efficiency and sustainability.
Abstract
RoadSens is a platform designed to expedite the digitalization process of forest roads, a cornerstone of efficient forest operations and management. We incorporate stereo-vision spatial mapping and deep-learning image segmentation to extract, measure, and analyze various geometric features of the roads. The features are precisely georeferenced by fusing post-processing results of an integrated global navigation satellite system (GNSS) module and odometric localization data obtained from the stereo camera. The first version of RoadSens, RSv1, provides measurements of longitudinal slope, horizontal/vertical radius of curvature and various cross-sectional parameters, e.g., visible road width, centerline/midpoint positions, left and right sidefall slopes, and the depth and distance of visible ditches from the road’s edges. The potential of RSv1 is demonstrated and validated through its application to two road segments in southern Norway. The results highlight a promising performance. The trained image segmentation model detects the road surface with the precision and recall values of 96.8 and 81.9 , respectively. The measurements of visible road width indicate sub-decimeter level inter-consistency and 0.38 m median accuracy. The cross-section profiles over the road surface show 0.87 correlation and 9.8 cm root mean squared error (RMSE) against ground truth. The RSv1’s georeferenced road midpoints exhibit an overall accuracy of 21.6 cm in horizontal direction. The GNSS height measurements, which are used to derive longitudinal slope and vertical curvature exhibit an average error of 5.7 cm compared to ground truth. The study also identifies and discusses the limitations and issues of RSv1, which provide useful insights into the challenges in future versions.
Abstract
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