Accurate and reliable ego-localization is critical for autonomous driving. In
this paper, we present EgoVM, an end-to-end localization network that achieves
comparable localization accuracy to prior state-of-the-art methods, but uses
lightweight vectorized maps instead of heavy point-based maps. To begin with,
we extract BEV features from online multi-view images and LiDAR point cloud.
Then, we employ a set of learnable semantic embeddings to encode the semantic
types of map elements and supervise them with semantic segmentation, to make
their feature representation consistent with BEV features. After that, we feed
map queries, composed of learnable semantic embeddings and coordinates of map
elements, into a transformer decoder to perform cross-modality matching with
BEV features. Finally, we adopt a robust histogram-based pose solver to
estimate the optimal pose by searching exhaustively over candidate poses. We
comprehensively validate the effectiveness of our method using both the
nuScenes dataset and a newly collected dataset. The experimental results show
that our method achieves centimeter-level localization accuracy, and
outperforms existing methods using vectorized maps by a large margin.
Furthermore, our model has been extensively tested in a large fleet of
autonomous vehicles under various challenging urban scenes.

有关自动驾驶的精准可靠的自我定位非常重要。本文介绍了一种名为 EgoVM 的端到端定位网络，它通过使用轻量级矢量化地图而不是繁重的基于点的地图，实现了与先前最先进方法相当的定位精度。