LiDAR-Based SLAM for Autonomous Driving: A Survey

Simultaneous Localization and Mapping (SLAM) serves as a pivotal technology in autonomous driving. Unlike visual SLAM, which struggles in settings with inadequate visual information, LiDAR-based SLAM captures point cloud data directly, rendering a three-dimensional representation of the environment and thus offering a more robust localization capability. Given the swift advancements in autonomous driving, this work highlights the potential of combining LiDAR-based SLAM with deep learning methods, particularly in terms of improved perception and navigation within complex environments. The survey concludes by highlighting unresolved challenges and providing a practical outlook on the future of autonomous driving.