An In-Depth Examination of SLAM Methods: Challenges, Advancements, and Applications in Complex Scenes for Autonomous Driving

Precise environmental perception and reliable real-time localization are crucial for achieving advanced driver assistance functions. Against this backdrop, Simultaneous Localization and Mapping (SLAM), with its unique advantages, has become one of the indispensable key technologies in the field of autonomous driving (AD). However, as the traffic environment for autonomous vehicles (AVs) continues to become increasingly complex and diverse, such as urban streets, highways, and adverse weather conditions, these challenges pose higher demands on vehicles’ localization and mapping capabilities, also bring new opportunities and challenges for further optimization and application of SLAM. In this article, we conduct a comprehensive and in-depth analysis of the current research status and applications of SLAM in complex scenes of AD. First, we explore the challenges faced by AVs in various complex scenes, including the interference of dynamic objects, the precise localization and mapping requirements in large-scale scenes, as well as variable environmental and weather conditions. Subsequently, we delve into the coping strategies and methods of SLAM in these specific scenes. Finally, we compare and summarize the datasets related to SLAM in complex scenes of AD and point out some potential research directions for achieving high-level AD. We hope that this study will track and update the latest progress in SLAM for AVs. To promote the development of the open source community and future academic research, we created a repository <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/herofly1/CQU-AVL-SLAM</uri> that provides related review papers and methodological resources.