Precise Localization in High-Definition Road Maps for Urban Regions

The future of automated driving in urban areas will most probably rely on highly accurate road maps. However, the necessary precision of a localization in such maps has so far only been reached using extra, sensor specific feature layers for localization. In this paper we want to show that it is possible to achieve sufficient accuracy without a separate localization layer. Instead, elements are used that are already contained in high-resolution road maps, such as markings and road borders. For this, we introduce a modular approach in which detections from different detection algorithms are associated with elements in the map and then fused to an absolute pose using an Unscented Kalman Filter. We evaluate our approach using a sensor setup that employs a stereo camera, vehicle odometry and a low-cost GNSS module on a 5km test route covering both narrow urban roads and multi-lane main roads under varying weather conditions. The results show that this approach is capable to be used for highly automated driving, showing an accuracy of 0.08m in typical road scenarios and a is available 98% of the time.