Accurate and reliable localization in extensive outdoor environments will be a key ability of future driver assistance systems and autonomously driving vehicles. Relative localization, using sensors and a pre-mapped environment, will play a crucial role for such systems, because standard global navigation satellite system (GNSS) solutions will not be able to provide the required reliability. However, it is obvious that the environment maps will have to be quite detailed, making it a must to produce them fully automatically. In this paper, a relative localization approach is evaluated for an environment of substantial extent. The pre-mapped environment is obtained using a LIDAR mobile mapping van. From the raw data, landmarks are extracted fully automatically and inserted into a landmark map. Then, in a second campaign, a robotic vehicle is used to traverse the same scene. Landmarks are extracted from the sensor data of this vehicle as well. Using associated landmark pairs and an estimation approach, the positions of the robotic vehicle are obtained. The number of matches and the matching errors are analyzed, and it is shown that localization based on landmarks outperforms the vehicle's standard GNSS solution.