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Lidar-based simultaneous localization and mapping (SLAM) approaches haveobtained considerable success in autonomous robotic systems. This is in partowing to the high-accuracy of robust SLAM algorithms and the emergence of newand lower-cost lidar products. This study benchmarks current state-of-the-artlidar SLAM algorithms with a multi-modal lidar sensor setup showcasing diversescanning modalities (spinning and solid-state) and sensing technologies, andlidar cameras, mounted on a mobile sensing and computing platform. We extendour previous multi-modal multi-lidar dataset with additional sequences and newsources of ground truth data. Specifically, we propose a new multi-modalmulti-lidar SLAM-assisted and ICP-based sensor fusion method for generatingground truth maps. With these maps, we then match real-time pointcloud datausing a natural distribution transform (NDT) method to obtain the ground truthwith full 6 DOF pose estimation. This novel ground truth data leverageshigh-resolution spinning and solid-state lidars. We also include new open roadsequences with GNSS-RTK data and additional indoor sequences with motioncapture (MOCAP) ground truth, complementing the previous forest sequences withMOCAP data. We perform an analysis of the positioning accuracy achieved withten different SLAM algorithm and lidar combinations. We also report theresource utilization in four different computational platforms and a total offive settings (Intel and Jetson ARM CPUs). Our experimental results show thatcurrent state-of-the-art lidar SLAM algorithms perform very differently fordifferent types of sensors. More results, code, and the dataset can be foundat:\href{https://github.com/TIERS/tiers-lidars-dataset-enhanced}{github.com/TIERS/tiers-lidars-dataset-enhanced.

A Benchmark for Multi-Modal Lidar SLAM with Ground Truth in GNSS-Denied Environments