  We address the localization of robots in a multi-MAV system where external
infrastructure like GPS or motion capture systems may not be available. Our
approach lends itself to implementation on platforms with several constraints
on size, weight, and power (SWaP). Particularly, our framework fuses the
onboard VIO with the anonymous, visual-based robot-to-robot detection to
estimate all robot poses in one common frame, addressing three main challenges:
1) the initial configuration of the robot team is unknown, 2) the data
association between each vision-based detection and robot targets is unknown,
and 3) the vision-based detection yields false negatives, false positives,
inaccurate, and provides noisy bearing, distance measurements of other robots.
Our approach extends the Coupled Probabilistic Data Association Filter
(CPDAF)[1] to cope with nonlinear measurements. We demonstrate the superior
performance of our approach over a simple VIO-based method in a simulation with
the measurement models statistically modeled using the real experimental data.
We also show how onboard sensing, estimation, and control can be used for
formation flight.
