  The Unmanned Aerial Vehicle (UAV) swarm networks will play a crucial role in
the B5G/6G network thanks to its appealing features, such as wide coverage and
on-demand deployment. Emergency communication (EC) is essential to promptly
inform UAVs of potential danger to avoid accidents, whereas the conventional
communication-only feedback-based methods, which separate the digital and
physical identities (DPI), bring intolerable latency and disturb the unintended
receivers. In this paper, we present a novel DPI-Mapping solution to match the
identities (IDs) of UAVs from dual domains for visual networking, which is the
first solution that enables UAVs to communicate promptly with what they see
without the tedious exchange of beacons. The IDs are distinguished dynamically
by defining feature similarity, and the asymmetric IDs from different domains
are matched via the proposed bio-inspired matching algorithm. We also consider
Kalman filtering to combine the IDs and predict the states for accurate
mapping. Experiment results show that the DPI-Mapping reduces individual
inaccuracy of features and significantly outperforms the conventional
broadcast-based and feedback-based methods in EC latency. Furthermore, it also
reduces the disturbing messages without sacrificing the hit rate.
