  Lidar technology has evolved significantly over the last decade, with higher
resolution, better accuracy, and lower cost devices available today. In
addition, new scanning modalities and novel sensor technologies have emerged in
recent years. Public datasets have enabled benchmarking of algorithms and have
set standards for the cutting edge technology. However, existing datasets are
not representative of the technological landscape, with only a reduced number
of lidars available. This inherently limits the development and comparison of
general-purpose algorithms in the evolving landscape. This paper presents a
novel multi-modal lidar dataset with sensors showcasing different scanning
modalities (spinning and solid-state), sensing technologies, and lidar cameras.
The focus of the dataset is on low-drift odometry, with ground truth data
available in both indoors and outdoors environment with sub-millimeter accuracy
from a motion capture (MOCAP) system. For comparison in longer distances, we
also include data recorded in larger spaces indoors and outdoors. The dataset
contains point cloud data from spinning lidars and solid-state lidars. Also, it
provides range images from high resolution spinning lidars, RGB and depth
images from a lidar camera, and inertial data from built-in IMUs. This is, to
the best of our knowledge, the lidar dataset with the most variety of sensors
and environments where ground truth data is available. This dataset can be
widely used in multiple research areas, such as 3D LiDAR simultaneous
localization and mapping (SLAM), performance comparison between multi-modal
lidars, appearance recognition and loop closure detection. The datasets are
available at: https://github.com/TIERS/tiers-lidars-dataset.
