Urban road localization by using multiple layer map matching and line segment matching

In recent years, automated vehicle researches move on to the next stage, that is, auto-driving experiments on public roads. This study focuses on how to realize accurate localization based on the use of Lidar data and precise map. On different roads such as urban roads and expressways, the observed information of surrounding is significantly different. For example, on the urban roads, many buildings can be observed around the upper part of the vehicle. Such observation realizes accurate map matching. On the other hand, the upper part has no specific observation on the expressway. Therefore, it is necessary to observe the lower part for the map matching. To adapt the situation changes, we propose a localization method based on self-adaptive multi-layered scan matching and road line segment matching. The main idea is to effectively match the features observed from different heights and to improve the results by applying the line segment matching in certain scenes. Localization experiments show the ability to estimate accurate vehicle pose in urban driving.