Local Map Construction with SDMap: A Comprehensive Survey

TL;DR

This survey reviews local map construction using SDMap in intelligent driving, discussing methods, datasets, data fusion, challenges, and future directions to improve robustness and scalability.

Contribution

It provides a comprehensive overview of SDMap-based local map construction, including definitions, processing flow, datasets, and analysis of multimodal data fusion techniques.

Findings

Analyzes key challenges in SDMap processing and spatial alignment.

Highlights future research directions like road topology inference.

Discusses the potential of SDMap for low-cost, accessible local mapping.

Abstract

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.