Guardrail detection for landmark-based localization

Abstract: A requirement for safe autonomous driving is to have an accurate global localization of the ego vehicle. Methods based on Global Navigation Satellite System (GNSS) are the most common but are not precise enough in areas without good satellite signals. Instead, methods likelandmark-based localization (LBL) can be used. In LBL, sensors onboard the vehicle detectlandmarks near the vehicle. With these detections, the vehicle’s position is deduced by looking up matching landmarks on a high-definition map. Commonly found along roads, stretching for long distances, guardrails are a great landmark that can be used for LBL. In this thesis, two different methods are proposed to detect and vectorize guardrails from vehicle sensor data to enable future map matching for LBL. The first method uses semantically labeled LiDAR data with pre-classified guardrail LiDAR points as input data. The method is based on the DBSCAN clustering algorithm to cluster and filter out false positives from the pre-classified LiDAR points. The second algorithm uses raw LiDAR data as input. The algorithm finds guardrail candidate points by segmenting high-densityareas and matching these with thresholds taken from the geometry of guardrails. Similar to the first method, these are then clustered into guardrail clusters. The clusters are then vectorized into the wanted output of a 2D vector, corresponding to points inside the guardrail with aspecific interval. To evaluate the performance of the proposed algorithms, simulations from real-life data are analyzed in both a quantitative and qualitative way. The qualitative experiments showcase that both methods perform well even in difficult scenarios. Timings of the simulations show that both methods are fast enough to be applicable in real-time use cases. The defined performance measures show that the method using raw LiDAR data is more robust and manages to detect more and longer parts of the guardrails.