A key component of graph-based SLAM systems is the ability to detect loop
closures in a trajectory to reduce the drift accumulated over time from the
odometry. Most LiDAR-based methods achieve this goal by using only the
geometric information, disregarding the semantics of the scene. In this work,
we introduce PADLoC, a LiDAR-based loop closure detection and registration
architecture comprising a shared 3D convolutional feature extraction backbone,
a global descriptor head for loop closure detection, and a novel
transformer-based head for point cloud matching and registration. We present
multiple methods for estimating the point-wise matching confidence based on
diversity indices. Additionally, to improve forward-backward consistency, we
propose the use of two shared matching and registration heads with their source
and target inputs swapped by exploiting that the estimated relative
transformations must be inverse of each other. Furthermore, we leverage
panoptic information during training in the form of a novel loss function that
reframes the matching problem as a classification task in the case of the
semantic labels and as a graph connectivity assignment for the instance labels.
We perform extensive evaluations of PADLoC on multiple real-world datasets
demonstrating that it achieves state-of-the-art performance. The code of our
work is publicly available at http://padloc.cs.uni-freiburg.de