  This paper presents novel strategies for spawning and fusing submaps within
an elastic dense 3D reconstruction system. The proposed system uses spatial
understanding of the scanned environment to control memory usage growth by
fusing overlapping submaps in different ways. This allows the number of submaps
and memory consumption to scale with the size of the environment rather than
the duration of exploration. By analysing spatial overlap, our system segments
distinct spaces, such as rooms and stairwells on the fly during exploration.
Additionally, we present a new mathematical formulation of relative uncertainty
between poses to improve the global consistency of the reconstruction.
Performance is demonstrated using a multi-floor multi-room indoor experiment, a
large-scale outdoor experiment and simulated datasets. Relative to our
baseline, the presented approach demonstrates improved scalability and
accuracy.
