  We present a vehicle self-localization method using point-based deep neural
networks. Our approach processes measurements and point features, i.e.
landmarks, from a high-definition digital map to infer the vehicle's pose. To
learn the best association and incorporate local information between the point
sets, we propose an attention mechanism that matches the measurements to the
corresponding landmarks. Finally, we use this representation for the
point-cloud registration and the subsequent pose regression task. Furthermore,
we introduce a training simulation framework that artificially generates
measurements and landmarks to facilitate the deployment process and reduce the
cost of creating extensive datasets from real-world data. We evaluate our
method on our dataset, as well as an adapted version of the Kitti odometry
dataset, where we achieve superior performance compared to related approaches;
and additionally show dominant generalization capabilities.
