A Lightweight Feature Map Creation Method for Intelligent Vehicle Localization in Urban Road Environments

Accurate and reliable localization in urban environments is critical for high-level autonomous driving systems. In comparison to other localization strategies, map-based methods are more reliable and have higher localization accuracy. However, due to higher storage and update costs, dense map-based localization methods are inefficient for widespread use. In urban road environments, structural features, such as road curbs that limit the road passable area, and pole-like features, such as streetlights, tree trunks, and traffic lights, exhibit exceptional long-term stability and extraction consistency. The maps based on road curbs and pole-like features are unaffected by dynamic obstacles, such as vehicles and pedestrians, making them ideal for vehicle localization in urban environments. In this article, we design a lightweight feature map for LiDAR localization in urban environments based on road curbs and pole-like features, and propose a novel, fast, and accurate extraction strategy for these features, implementing the entire pipeline of feature extraction, map construction, and localization. We combine the orderliness of 2-D range images with the spatial properties of 3-D point clouds to extract road curbs and poles quickly and accurately. The proposed feature extraction methods and localization system are evaluated on datasets collected in multiple environments and times. The experimental results demonstrate that the method proposed in this article is more efficient and robust at features' extraction and vehicle localization compared with other state-of-the-art approaches.

Automated summary

Accurate and reliable localization in urban environments is crucial for autonomous driving systems. This article proposes a LiDAR localization method based on road curbs and pole-like features, along with a lightweight feature map and a novel extraction strategy. Experimental results demonstrate the method's efficiency and robustness in feature extraction and vehicle localization, outperforming existing approaches.