  Mapping and navigation have gone hand-in-hand since long before robots
existed. Maps are a key form of communication, allowing someone who has never
been somewhere to nonetheless navigate that area successfully. In the context
of multi-robot systems, the maps and information that flow between robots are
necessary for effective collaboration, whether those robots are operating
concurrently, sequentially, or completely asynchronously. In this paper, we
argue that maps must go beyond encoding purely geometric or visual information
to enable increasingly complex autonomy, particularly between robots. We
propose a framework for multi-robot autonomy, focusing in particular on air and
ground robots operating in outdoor 2.5D environments. We show that semantic
maps can enable the specification, planning, and execution of complex
collaborative missions, including localization in GPS-denied settings. A
distinguishing characteristic of this work is that we strongly emphasize field
experiments and testing, and by doing so demonstrate that these ideas can work
at scale in the real world. We also perform extensive simulation experiments to
validate our ideas at even larger scales. We believe these experiments and the
experimental results constitute a significant step forward toward advancing the
state-of-the-art of large-scale, collaborative multi-robot systems operating
with real communication, navigation, and perception constraints.
