  Unmanned aerial vehicles (UAVs) have been used for many applications in
recent years, from urban search and rescue, to agricultural surveying, to
autonomous underground mine exploration. However, deploying UAVs in tight,
indoor spaces, especially close to humans, remains a challenge. One solution,
when limited payload is required, is to use micro-UAVs, which pose less risk to
humans and typically cost less to replace after a crash. However, micro-UAVs
can only carry a limited sensor suite, e.g. a monocular camera instead of a
stereo pair or LiDAR, complicating tasks like dense mapping and markerless
multi-person 3D human pose estimation, which are needed to operate in tight
environments around people. Monocular approaches to such tasks exist, and dense
monocular mapping approaches have been successfully deployed for UAV
applications. However, despite many recent works on both marker-based and
markerless multi-UAV single-person motion capture, markerless single-camera
multi-person 3D human pose estimation remains a much earlier-stage technology,
and we are not aware of existing attempts to deploy it in an aerial context. In
this paper, we present what is thus, to our knowledge, the first system to
perform simultaneous mapping and multi-person 3D human pose estimation from a
monocular camera mounted on a single UAV. In particular, we show how to loosely
couple state-of-the-art monocular depth estimation and monocular 3D human pose
estimation approaches to reconstruct a hybrid map of a populated indoor scene
in real time. We validate our component-level design choices via extensive
experiments on the large-scale ScanNet and GTA-IM datasets. To evaluate our
system-level performance, we also construct a new Oxford Hybrid Mapping dataset
of populated indoor scenes.
