  This article introduces an approach to facilitate cooperative exploration and
mapping of large-scale, near-ground, underground, or indoor spaces via a novel
integration framework for locally-dense agent map data. The effort targets
limited Size, Weight, and Power (SWaP) agents with an emphasis on limiting
required communications and redundant processing. The approach uses a unique
organization of batch optimization engines to enable a highly efficient
two-tier optimization structure. Tier I consist of agents that create and
potentially share local maplets (local maps, limited in size) which are
generated using Simultaneous Localization and Mapping (SLAM) map-building
software and then marginalized to a more compact parameterization. Maplets are
generated in an overlapping manner and used to estimate the transform and
uncertainty between those overlapping maplets, providing accurate and compact
odometry or delta-pose representation between maplet's local frames. The delta
poses can be shared between agents, and in cases where maplets have salient
features (for loop closures), the compact representation of the maplet can also
be shared.
  The second optimization tier consists of a global optimizer that seeks to
optimize those maplet-to-maplet transformations, including any loop closures
identified. This can provide an accurate global "skeleton"' of the traversed
space without operating on the high-density point cloud. This compact version
of the map data allows for scalable, cooperative exploration with limited
communication requirements where most of the individual maplets, or low
fidelity renderings, are only shared if desired.
