Lidar scan feature for localization with highly precise 3-D map

In recent years, automated vehicle researches move on to the next stage, that is auto-driving experiments on public roads. Major challenge is how to robustly drive at complicated situations such as narrow or non-featured road. In order to realize practical performance, some static information should be kept on memory such as road topology, building shape, white line, curb, traffic light and so on. Currently, some measurement companies have already begun to prepare map database for automated vehicles. They are able to provide highly-precise 3-D map for robust automated driving. This study focuses on what kind of data should be observed during automated driving with such precise database. In particular, we focus on the accurate localization based on the use of lidar data and precise 3-D map, and propose a feature quantity for scan data based on distribution of clusters. Localization experiment shows that our method can measure surrounding uncertainty and guarantee accurate localization.