在线构建全局矢量高精地图，无需人工干预。
GlobalMapNet: An Online Framework for Vectorized Global HD Map Construction
- 基于车辆实时数据持续合并局部地图，生成全局矢量地图
- 提出Map NMS算法消除重复元素，提升地图清晰度
- 适用于自动驾驶系统实时地图更新，适合车载部署
高精地图对自动驾驶至关重要。传统建图流程昂贵且难以扩展。近年来，众包和在线建图成为替代方案，但各有局限。本文提出一种全新的全局地图构建方法，直接生成矢量化的全球高精地图，融合众包与在线建图的优势。我们提出GlobalMapNet，首个面向车载的矢量全局高精地图在线构建框架，可在本车实时更新并使用全局地图。为从零开始构建全局地图，我们设计GlobalMapBuilder，持续匹配并合并局部地图；提出新算法Map NMS，剔除重复地图元素，生成清晰地图；还提出GlobalMapFusion，聚合历史地图信息，提升预测一致性。我们在Argoverse2和nuScenes两个主流数据集上验证，结果表明该框架可生成全局一致的地图。
原文摘要 · Abstract (English)
High-definition (HD) maps are essential for autonomous driving systems. Traditionally, an expensive and labor-intensive pipeline is implemented to construct HD maps, which is limited in scalability. In recent years, crowdsourcing and online mapping have emerged as two alternative methods, but they have limitations respectively. In this paper, we provide a novel methodology, namely global map construction, to perform direct generation of vectorized global maps, combining the benefits of crowdsourcing and online mapping. We introduce GlobalMapNet, the first online framework for vectorized global HD map construction, which updates and utilizes a global map on the ego vehicle. To generate the global map from scratch, we propose GlobalMapBuilder to match and merge local maps continuously. We design a new algorithm, Map NMS, to remove duplicate map elements and produce a clean map. We also propose GlobalMapFusion to aggregate historical map information, improving consistency of prediction. We examine GlobalMapNet on two widely recognized datasets, Argoverse2 and nuScenes, showing that our framework is capable of generating globally consistent results.
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