  Place recognition is crucial for robot localization and loop closure in
simultaneous localization and mapping (SLAM). Light Detection and Ranging
(LiDAR), known for its robust sensing capabilities and measurement consistency
even in varying illumination conditions, has become pivotal in various fields,
surpassing traditional imaging sensors in certain applications. Among various
types of LiDAR, spinning LiDARs are widely used, while non-repetitive scanning
patterns have recently been utilized in robotics applications. Some LiDARs
provide additional measurements such as reflectivity, Near Infrared (NIR), and
velocity from Frequency modulated continuous wave (FMCW) LiDARs. Despite these
advances, there is a lack of comprehensive datasets reflecting the broad
spectrum of LiDAR configurations for place recognition. To tackle this issue,
our paper proposes the HeLiPR dataset, curated especially for place recognition
with heterogeneous LiDARs, embodying spatiotemporal variations. To the best of
our knowledge, the HeLiPR dataset is the first heterogeneous LiDAR dataset
supporting inter-LiDAR place recognition with both non-repetitive and spinning
LiDARs, accommodating different field of view (FOV)s and varying numbers of
rays. The dataset covers diverse environments, from urban cityscapes to
high-dynamic freeways, over a month, enhancing adaptability and robustness
across scenarios. Notably, HeLiPR includes trajectories parallel to MulRan
sequences, making it valuable for research in heterogeneous LiDAR place
recognition and long-term studies. The dataset is accessible at
https://sites.google.com/view/heliprdataset.
