 This paper presents a comprehensive evaluation of the state-of-the-art LIDAR SLAM algorithms for various combinations of LIDAR sensors and SLAM algorithms. The authors benchmarked the algorithms with a multi-modal LIDAR sensor setup, including spinning in solid-state LIDARs, as well as LIDAR cameras, mounted on a mobile sensing and computing platform. They extended their previous multimodal multi-leadier dataset with additional sequences and new sources of ground truth data. Additionally, they proposed a new multimodal multi-leadar SLAM-assisted and ICP-based sensor fusion method for generating ground truth maps. With these maps, they matched real-time point cloud data using a normal distributions transform, NDT, method to obtain the ground truth with a full six degrees of freedom, six DOEF, pose estimation. Furthermore, they included new open-road sequences with global navigation satellite system real-time kinematic, GNSS-RTK, data and additional indoor sequences with motion capture, MOCAP, ground truth, complementing the previous forest sequences with MOCAP data. Finally, they performed an analysis of the positioning accuracy achieved, COMPRA. This article was authored by Ha Seer, Ching Ching Lee, Shien Jia Yu, and others. We are article.tv, links in the description below.