SAR data based Landmark Navigation for highly precise Vehicle Localization
This approach deals with a probabilistic state estimation method with the aid of landmarks in the context of autonomous driving and driver assistance systems. It is mandatory to get a precise ego position in order to operate vehicles throughout complex urban environments where global positioning methods like GPS provide only low position accuracies and low reliability. On board sensors like LiDAR are able to detect a wide range of objects in the surrounding of a vehicle. Static urban objects like streetlamps or traffic light posts with absolute coordinates could be a solution to improve stability on the localization of a vehicle. Modern remote sensing approaches like geodetic radar are able to extract certain features like pole shaped objects in an extensive and fast way. This work demonstrates the possibility of locating oneself within a landmark map based on a low cost LiDAR sensor. Monte Carlo localization enhances GPS positioning accuracy and was successfully applied on a test site.