Accurate and robust localization remains a significant challenge for
autonomous vehicles. The cost of sensors and limitations in local computational
efficiency make it difficult to scale to large commercial applications.
Traditional vision-based approaches focus on texture features that are
susceptible to changes in lighting, season, perspective, and appearance.
Additionally, the large storage size of maps with descriptors and complex
optimization processes hinder system performance. To balance efficiency and
accuracy, we propose a novel lightweight visual semantic localization algorithm
that employs stable semantic features instead of low-level texture features.
First, semantic maps are constructed offline by detecting semantic objects,
such as ground markers, lane lines, and poles, using cameras or LiDAR sensors.
Then, online visual localization is performed through data association of
semantic features and map objects. We evaluated our proposed localization
framework in the publicly available KAIST Urban dataset and in scenarios
recorded by ourselves. The experimental results demonstrate that our method is
a reliable and practical localization solution in various autonomous driving
localization tasks.

提出了一种新颖的轻量级视觉语义定位算法，通过稳定的语义特征代替低级纹理特征，结合离线构建的语义地图和在线的数据关联实现实时的自动驾驶定位。实验结果表明，该方法在各种自动驾驶定位任务中是可靠且实用的定位解决方案。

自主车辆的单目语义地图定位

Monocular Localization with Semantics Map for Autonomous Vehicles

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.

本文提出了一种基于多视角摄像机图像的端到端视觉语义定位神经网络 BEV-Locator，并通过大规模 nuScenes 和 Qcraft 数据集的实验结果证明该方法能够有效地关联来自多视角图像和全局语义地图的跨模态信息，可以在多种场景下估计车辆姿态，实验结果表明其横向、长向平移和航向角度的平均绝对误差分别为 0.052m，0.135m 和 0.251 度。