  Swarm robots have sparked remarkable developments across a range of fields.
While it is necessary for various applications in swarm robots, a fast and
robust coordinate initialization in vision-based drone swarms remains elusive.
To this end, our paper proposes a complete system to recover a swarm's initial
relative pose on platforms with size, weight, and power (SWaP) constraints. To
overcome limited coverage of field-of-view (FoV), the drones rotate in place to
obtain observations. To tackle the anonymous measurements, we formulate a
non-convex rotation estimation problem and transform it into a semi-definite
programming (SDP) problem, which can steadily obtain global optimal values.
Then we utilize the Hungarian algorithm to recover relative translation and
correspondences between observations and drone identities. To safely acquire
complete observations, we actively search for positions and generate feasible
trajectories to avoid collisions. To validate the practicability of our system,
we conduct experiments on a vision-based drone swarm with only stereo cameras
and inertial measurement units (IMUs) as sensors. The results demonstrate that
the system can robustly get accurate relative poses in real time with limited
onboard computation resources. The source code is released.
