  In this paper, we address bandwidth-limited and obstruction-prone
collaborative perception, specifically in the context of multi-agent semantic
segmentation. This setting presents several key challenges, including
processing and exchanging unregistered robotic swarm imagery. To be successful,
solutions must effectively leverage multiple non-static and
intermittently-overlapping RGB perspectives, while heeding bandwidth
constraints and overcoming unwanted foreground obstructions. As such, we
propose an end-to-end learn-able Multi-Agent Spatial Handshaking network (MASH)
to process, compress, and propagate visual information across a robotic swarm.
Our distributed communication module operates directly (and exclusively) on raw
image data, without additional input requirements such as pose, depth, or
warping data. We demonstrate superior performance of our model compared against
several baselines in a photo-realistic multi-robot AirSim environment,
especially in the presence of image occlusions. Our method achieves an absolute
11% IoU improvement over strong baselines.
