EFFECTIVE RAILROAD FRAGMENTATION AND INFRASTRUCTURE RECOGNITION BASED ON DENSE LIDAR POINT CLOUDS

Monitoring the condition of railway infrastructure is essential for maintaining safety standards and preventing accidents. The regular inspections required for this task are still typically carried out in many countries with costly and time-consuming on-site human inspections. LiDAR point clouds collected by mobile laser scanning (MLS) already proved to be suitable for recognizing important railroad infrastructure elements, such as cables and the rail tracks. However, the computational requirement for processing large data sets like these often extremely dense point clouds is still a challenge nowadays, resulting in longer execution time than practically applicable. In our research, we have implemented and comparatively analyzed railroad fragmentation and object segmentation algorithms with the focus on robustness and high effectiveness: prioritizing automation and prerequisite reduction (e.g. the spatial relationship between the position of the railway track and the overhead contact line). These aspects also enable the easy parallelization for the processing of larger railroad segments.