  This paper is about 3D pose estimation on LiDAR scans with extremely minimal
storage requirements to enable scalable mapping and localisation. We achieve
this by clustering all points of segmented scans into semantic objects and
representing them only with their respective centroid and semantic class. In
this way, each LiDAR scan is reduced to a compact collection of four-number
vectors. This abstracts away important structural information from the scenes,
which is crucial for traditional registration approaches. To mitigate this, we
introduce an object-matching network based on self- and cross-correlation that
captures geometric and semantic relationships between entities. The respective
matches allow us to recover the relative transformation between scans through
weighted Singular Value Decomposition (SVD) and RANdom SAmple Consensus
(RANSAC). We demonstrate that such representation is sufficient for metric
localisation by registering point clouds taken under different viewpoints on
the KITTI dataset, and at different periods of time localising between KITTI
and KITTI-360. We achieve accurate metric estimates comparable with
state-of-the-art methods with almost half the representation size, specifically
1.33 kB on average.
