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

Abstract

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 paper, 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 2D range images with the spatial properties of 3D 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 paper is more efficient and robust at features extraction and vehicle localization compared with other state-of-the-art approaches.