Precise pose graph localization with sparse point and lane features

We introduce a novel pose graph-based localization technique for autonomous driving that incorporates both sparse point features as well as lane markings on the road. Unlike many commonly used filter methods, our graph-based localization takes a much larger history of the trajectory into account. In addition, we utilize a multiple-hypothesis approach for data association for increased robustness against the presence of outliers. By incorporating both sparse point features and lane markings, we are able to take advantage of both kinds of features widely available in urban environments as well as highways. Furthermore, by using high level features, we avoid having to store and match against large amounts of data such as dense 3D point clouds. Finally, we evaluate our approach on real driving sequences on city roads.