  The collaboration of unmanned aerial vehicles (UAVs) has become a popular
research topic for its practicability in multiple scenarios. The collaboration
of multiple UAVs, which is also known as aerial swarm is a highly complex
system, which still lacks a state-of-art decentralized relative state
estimation method. In this paper, we present a novel fully decentralized
visual-inertial-UWB fusion framework for relative state estimation and
demonstrate the practicability by performing extensive aerial swarm flight
experiments. The comparison result with ground truth data from the motion
capture system shows the centimeter-level precision which outperforms all the
Ultra-WideBand (UWB) and even vision based method. The system is not limited by
the field of view (FoV) of the camera or Global Positioning System (GPS),
meanwhile on account of its estimation consistency, we believe that the
proposed relative state estimation framework has the potential to be
prevalently adopted by aerial swarm applications in different scenarios in
multiple scales.
