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.

翻译：精确且鲁棒的定位仍然是自动驾驶车辆面临的重要挑战。传感器成本与本地计算效率的限制使得大规模商业应用难以实现。传统的视觉方法主要依赖于纹理特征，这些特征易受光照、季节、视角及外观变化的影响。此外，带有描述符的地图存储空间庞大以及复杂的优化过程也制约了系统性能。为平衡效率与精度，本文提出了一种新颖的轻量级视觉语义定位算法，该算法采用稳定的语义特征替代底层纹理特征。首先，通过摄像头或LiDAR传感器检测语义对象（如地面标记、车道线和杆状物）离线构建语义地图。随后，通过语义特征与地图对象的数据关联实现在线视觉定位。我们在公开的KAIST Urban数据集及自行采集的场景中评估了所提出的定位框架。实验结果表明，该方法在各种自动驾驶定位任务中是一种可靠且实用的定位解决方案。