Local Map Construction with SDMap: A Comprehensive Survey
It addresses the problem of improving local map perception for autonomous vehicles, but it is incremental as it surveys existing approaches rather than introducing new solutions.
This paper reviews methods for constructing local maps using Standard Definition maps (SDMap) for intelligent driving perception, analyzing definitions, processing flows, datasets, multimodal data representation, and fusion techniques.
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.