综述基于标准地图的局部地图构建方法，助力自动驾驶定位与规划。
Local Map Construction with SDMap: A Comprehensive Survey
- 梳理标准地图在局部地图构建中的处理流程与数据融合方法
- 分析多模态数据融合与时空对齐等关键技术挑战
- 适合关注自动驾驶感知与地图构建的科研人员参考
局部地图构建是智能驾驶感知的关键环节，为车辆定位与路径规划提供必要参考。标准定义地图（SDMap）因其低成本、易获取和通用性强，具有作为局部地图感知先验信息的巨大潜力。本文系统回顾了基于SDMap的局部地图构建方法，涵盖定义、通用处理流程及常用数据集。同时，分析了多模态数据表示与融合策略在该场景下的应用。论文还探讨了当前关键挑战，如优化SDMap处理、提升与实时数据的空间对齐精度，以及融入更丰富的环境信息。最后展望未来研究方向，强调增强道路拓扑推理能力与多模态数据融合，以提升局部地图感知的鲁棒性与可扩展性。
原文摘要 · Abstract (English)
Local map construction is a vital component of intelligent driving perception, offering necessary reference for vehicle positioning and planning. Standard Definition map (SDMap), known for its low cost, accessibility, and versatility, has significant potential as prior information for local map perception. This paper mainly reviews the local map construction methods with SDMap, including definitions, general processing flow, and datasets. Besides, this paper analyzes multimodal data representation and fusion methods in SDMap-based local map construction. This paper also discusses key challenges and future directions, such as optimizing SDMap processing, enhancing spatial alignment with real-time data, and incorporating richer environmental information. At last, the review looks forward to future research focusing on enhancing road topology inference and multimodal data fusion to improve the robustness and scalability of local map perception.
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