  Accurate localization ability is fundamental in autonomous driving.
Traditional visual localization frameworks approach the semantic map-matching
problem with geometric models, which rely on complex parameter tuning and thus
hinder large-scale deployment. In this paper, we propose BEV-Locator: an
end-to-end visual semantic localization neural network using multi-view camera
images. Specifically, a visual BEV (Birds-Eye-View) encoder extracts and
flattens the multi-view images into BEV space. While the semantic map features
are structurally embedded as map queries sequence. Then a cross-model
transformer associates the BEV features and semantic map queries. The
localization information of ego-car is recursively queried out by
cross-attention modules. Finally, the ego pose can be inferred by decoding the
transformer outputs. We evaluate the proposed method in large-scale nuScenes
and Qcraft datasets. The experimental results show that the BEV-locator is
capable to estimate the vehicle poses under versatile scenarios, which
effectively associates the cross-model information from multi-view images and
global semantic maps. The experiments report satisfactory accuracy with mean
absolute errors of 0.052m, 0.135m and 0.251$^\circ$ in lateral, longitudinal
translation and heading angle degree.
