  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 for joint loop closure detection and registration in
LiDAR-based SLAM frameworks. We propose a novel transformer-based head for
point cloud matching and registration, and to leverage panoptic information
during training time. In particular, we propose a novel loss function that
reframes the matching problem as a classification task for the semantic labels
and as a graph connectivity assignment for the instance labels. During
inference, PADLoC does not require panoptic annotations, making it more
versatile than other methods. Additionally, we show that using two shared
matching and registration heads with their source and target inputs swapped
increases the overall performance by enforcing forward-backward consistency. We
perform extensive evaluations of PADLoC on multiple real-world datasets
demonstrating that it achieves state-of-the-art results. The code of our work
is publicly available at http://padloc.cs.uni-freiburg.de.
