  Accurate and robust state estimation is critical for autonomous navigation of
robot teams. This task is especially challenging for large groups of size,
weight, and power (SWAP) constrained aerial robots operating in
perceptually-degraded GPS-denied environments. We can, however, actively
increase the amount of perceptual information available to such robots by
augmenting them with a small number of more expensive, but less
resource-constrained, agents. Specifically, the latter can serve as sources of
perceptual information themselves. In this paper, we study the problem of
optimally positioning (and potentially navigating) a small number of more
capable agents to enhance the perceptual environment for their
lightweight,inexpensive, teammates that only need to rely on cameras and IMUs.
We propose a numerically robust, computationally efficient approach to solve
this problem via nonlinear optimization. Our method outperforms the standard
approach based on the greedy algorithm, while matching the accuracy of a
heuristic evolutionary scheme for global optimization at a fraction of its
running time. Ultimately, we validate our solution in both photorealistic
simulations and real-world experiments. In these experiments, we use
lidar-based autonomous ground vehicles as the more capable agents, and
vision-based aerial robots as their SWAP-constrained teammates. Our method is
able to reduce drift in visual-inertial odometry by as much as 90%, and it
outperforms random positioning of lidar-equipped agents by a significant
margin. Furthermore, our method can be generalized to different types of robot
teams with heterogeneous perception capabilities. It has a wide range of
applications, such as surveying and mapping challenging dynamic environments,
and enabling resilience to large-scale perturbations that can be caused by
earthquakes or storms.
