  Lidar-based simultaneous localization and mapping (SLAM) approaches have
obtained considerable success in autonomous robotic systems. This is in part
owing to the high-accuracy of robust SLAM algorithms and the emergence of new
and lower-cost lidar products. This study benchmarks current state-of-the-art
lidar SLAM algorithms with a multi-modal lidar sensor setup showcasing diverse
scanning modalities (spinning and solid-state) and sensing technologies, and
lidar cameras, mounted on a mobile sensing and computing platform. We extend
our previous multi-modal multi-lidar dataset with additional sequences and new
sources of ground truth data. Specifically, we propose a new multi-modal
multi-lidar SLAM-assisted and ICP-based sensor fusion method for generating
ground truth maps. With these maps, we then match real-time pointcloud data
using a natural distribution transform (NDT) method to obtain the ground truth
with full 6 DOF pose estimation. This novel ground truth data leverages
high-resolution spinning and solid-state lidars. We also include new open road
sequences with GNSS-RTK data and additional indoor sequences with motion
capture (MOCAP) ground truth, complementing the previous forest sequences with
MOCAP data. We perform an analysis of the positioning accuracy achieved with
ten different SLAM algorithm and lidar combinations. We also report the
resource utilization in four different computational platforms and a total of
five settings (Intel and Jetson ARM CPUs). Our experimental results show that
current state-of-the-art lidar SLAM algorithms perform very differently for
different types of sensors. More results, code, and the dataset can be found
at:
\href{https://github.com/TIERS/tiers-lidars-dataset-enhanced}{github.com/TIERS/tiers-lidars-dataset-enhanced.
