  Multi-robot systems such as swarms of aerial robots are naturally suited to
offer additional flexibility, resilience, and robustness in several tasks
compared to a single robot by enabling cooperation among the agents. To enhance
the autonomous robot decision-making process and situational awareness,
multi-robot systems have to coordinate their perception capabilities to
collect, share, and fuse environment information among the agents in an
efficient and meaningful way such to accurately obtain context-appropriate
information or gain resilience to sensor noise or failures. In this paper, we
propose a general-purpose Graph Neural Network (GNN) with the main goal to
increase, in multi-robot perception tasks, single robots' inference perception
accuracy as well as resilience to sensor failures and disturbances. We show
that the proposed framework can address multi-view visual perception problems
such as monocular depth estimation and semantic segmentation. Several
experiments both using photo-realistic and real data gathered from multiple
aerial robots' viewpoints show the effectiveness of the proposed approach in
challenging inference conditions including images corrupted by heavy noise and
camera occlusions or failures.
