Model-based rail detection in mobile laser scanning data

Similar to autonomous vehicles, future train applications require an accurate on-board self-localization for railway vehicles. Therefore, a reliable and real-time capable environment perception is required. In particular, the knowledge of the track taken at a turnout overcomes ambiguities in self-localization. As the most important groundwork for this, the paper introduces a new approach for the detection of rails and tracks solely from 2d lidar measurements. The technique is based on a new feature point method for lidar data, a template matching approach, and a spatial clustering technique to extract rails and tracks from the detected rail elements. The new approach is evaluated on six different datasets taken outdoors at a demanding test ground. It provides reliable and accurate detection results with centimeter accuracy, a recall of about 90 %, and a precision of about 95 %. The approach is able to detect rails even in complex real-world topologies such as at turnouts and even on tracks with more than two rails.