A benchmark of the leading LiDAR-IMU SLAM algorithms on the UNNC dataset

LiDAR-inertial Simultaneous Localization and Mapping (SLAM) has emerged as a critical technology for autonomous systems, offering robust performance in diverse environments. However, the relative performance of state-of-the-art algorithms in real-world scenarios remains under-explored. This paper presents a comprehensive bench-marking study of three leading LiDAR-inertial SLAM algorithms using a novel dataset collected on the University of Nottingham Ningbo China (UNNC) campus. The dataset encompasses a wide range of challenging scenarios, providing a realistic testbed for SLAM performance evaluation. We conduct a thorough analysis of the algorithms’ performances. Our results reveal the performance variations among the algorithms. This study not only provides valuable insights for practitioners in choosing appropriate SLAM solutions but also highlights areas for future research and improvement in LiDAR-inertial SLAM technology.