  This paper is about extremely robust and lightweight localisation using LiDAR
point clouds based on instance segmentation and graph matching. We model 3D
point clouds as fully-connected graphs of semantically identified components
where each vertex corresponds to an object instance and encodes its shape.
Optimal vertex association across graphs allows for full 6-Degree-of-Freedom
(DoF) pose estimation and place recognition by measuring similarity. This
representation is very concise, condensing the size of maps by a factor of 25
against the state-of-the-art, requiring only 3kB to represent a 1.4MB laser
scan. We verify the efficacy of our system on the SemanticKITTI dataset, where
we achieve a new state-of-the-art in place recognition, with an average of
88.4% recall at 100% precision where the next closest competitor follows with
64.9%. We also show accurate metric pose estimation performance - estimating
6-DoF pose with median errors of 10 cm and 0.33 deg.
