  The 3D LiDAR place recognition aims to estimate a coarse localization in a
previously seen environment based on a single scan from a rotating 3D LiDAR
sensor. The existing solutions to this problem include hand-crafted point cloud
descriptors (e.g., ScanContext, M2DP, LiDAR IRIS) and deep learning-based
solutions (e.g., PointNetVLAD, PCAN, LPDNet, DAGC, MinkLoc3D), which are often
only evaluated on accumulated 2D scans from the Oxford RobotCar dataset. We
introduce MinkLoc3D-SI, a sparse convolution-based solution that utilizes
spherical coordinates of 3D points and processes the intensity of 3D LiDAR
measurements, improving the performance when a single 3D LiDAR scan is used.
Our method integrates the improvements typical for hand-crafted descriptors
(like ScanContext) with the most efficient 3D sparse convolutions (MinkLoc3D).
Our experiments show improved results on single scans from 3D LiDARs (USyd
Campus dataset) and great generalization ability (KITTI dataset). Using
intensity information on accumulated 2D scans (RobotCar Intensity dataset)
improves the performance, even though spherical representation doesn't produce
a noticeable improvement. As a result, MinkLoc3D-SI is suited for single scans
obtained from a 3D LiDAR, making it applicable in autonomous vehicles.
