  This paper addresses the problem of active collaborative localization in
heterogeneous robot teams with unknown data association. It involves
positioning a small number of identical unmanned ground vehicles (UGVs) at
desired positions so that an unmanned aerial vehicle (UAV) can, through
unlabelled measurements of UGVs, uniquely determine its global pose. We model
the problem as a sequential two player game, in which the first player
positions the UGVs and the second identifies the two distinct hypothetical
poses of the UAV at which the sets of measurements to the UGVs differ by as
little as possible. We solve the underlying problem from the vantage point of
the first player for a subclass of measurement models using a mixture of local
optimization and exhaustive search procedures. Real-world experiments with a
team of UAV and UGVs show that our method can achieve centimeter-level global
localization accuracy. We also show that our method consistently outperforms
random positioning of UGVs by a large margin, with as much as a 90% reduction
in position and angular estimation error. Our method can tolerate a significant
amount of random as well as non-stochastic measurement noise. This indicates
its potential for reliable state estimation on board size, weight, and power
(SWaP) constrained UAVs. This work enables robust localization in
perceptually-challenged GPS-denied environments, thus paving the road for
large-scale multi-robot navigation and mapping.
