LIDROSE: LiDAR Inertial Data Collection With ROS For Open SourceSLAM Evaluation

In the rapidly advancing field of Autonomous Vehicles, achieving robust simultaneous localization and mapping (SLAM) capabilities stands as a fundamental goal. This paper explores the capabilities of LiDAR-based SLAM algorithms, presenting a comprehensive analysis of methodologies aimed at enhancing environmental perception and self-localization. Leveraging the precision and versatility of Light Detection and Ranging (LiDAR) technology, Autonomous Vehicles are able to accurately and precisely map their surrounding environment along with determining their own location within the map. Through a selected collection of state-of-the-art algorithms, we compare the approaches on our dataset of local experiments and showcase the results.