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。

EgoVM: 利用轻量级矢量化地图实现精确的自我定位

EgoVM: Achieving Precise Ego-Localization using Lightweight Vectorized  Maps

Autonomous driving systems require a good understanding of surrounding
environments, including moving obstacles and static High-Definition (HD)
semantic map elements. Existing methods approach the semantic map problem by
offline manual annotation, which suffers from serious scalability issues.
Recent learning-based methods produce dense rasterized segmentation predictions
to construct maps. However, these predictions do not include instance
information of individual map elements and require heuristic post-processing to
obtain vectorized maps. To tackle these challenges, we introduce an end-to-end
vectorized HD map learning pipeline, termed VectorMapNet. VectorMapNet takes
onboard sensor observations and predicts a sparse set of polylines in the
bird's-eye view. This pipeline can explicitly model the spatial relation
between map elements and generate vectorized maps that are friendly to
downstream autonomous driving tasks. Extensive experiments show that
VectorMapNet achieve strong map learning performance on both nuScenes and
Argoverse2 dataset, surpassing previous state-of-the-art methods by 14.2 mAP
and 14.6mAP. Qualitatively, we also show that VectorMapNet is capable of
generating comprehensive maps and capturing more fine-grained details of road
geometry. To the best of our knowledge, VectorMapNet is the first work designed
towards end-to-end vectorized map learning from onboard observations. Our
project website is available at
this https URL

本研究提出了一个名为 VectorMapNet 的端到端矢量化高清地图学习管道，它可以利用传感器观测数据预测出鸟瞰图中稀疏的折线。该管道可以明确地建模地图元素之间的空间关系，并生成对自主驾驶任务友好的矢量化高清地图。实验表明，VectorMapNet 在 nuScenes 和 Argoverse2 数据集上的地图学习性能优于以前的最先进方法，MAP 分别提高了 14.2 和 14.6。