Igor Spasojevic, Xu Liu, Alejandro Ribeiro, George J. Pappas, Vijay Kumar
Abstract
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.
Published in https://doi.org/10.48550/arXiv.2305.18193
