  In this paper, we address the multi-robot collaborative perception problem,
specifically in the context of multi-view infilling for distributed semantic
segmentation. This setting entails several real-world challenges, especially
those relating to unregistered multi-agent image data. Solutions must
effectively leverage multiple, non-static, and intermittently-overlapping RGB
perspectives. To this end, we propose the Multi-Agent Infilling Network: an
extensible neural architecture that can be deployed (in a distributed manner)
to each agent in a robotic swarm. Specifically, each robot is in charge of
locally encoding and decoding visual information, and an extensible neural
mechanism allows for an uncertainty-aware and context-based exchange of
intermediate features. We demonstrate improved performance on a realistic
multi-robot AirSim dataset.
