  We study the problem of multi-robot active mapping, which aims for complete
scene map construction in minimum time steps. The key to this problem lies in
the goal position estimation to enable more efficient robot movements. Previous
approaches either choose the frontier as the goal position via a myopic
solution that hinders the time efficiency, or maximize the long-term value via
reinforcement learning to directly regress the goal position, but does not
guarantee the complete map construction. In this paper, we propose a novel
algorithm, namely NeuralCoMapping, which takes advantage of both approaches. We
reduce the problem to bipartite graph matching, which establishes the node
correspondences between two graphs, denoting robots and frontiers. We introduce
a multiplex graph neural network (mGNN) that learns the neural distance to fill
the affinity matrix for more effective graph matching. We optimize the mGNN
with a differentiable linear assignment layer by maximizing the long-term
values that favor time efficiency and map completeness via reinforcement
learning. We compare our algorithm with several state-of-the-art multi-robot
active mapping approaches and adapted reinforcement-learning baselines.
Experimental results demonstrate the superior performance and exceptional
generalization ability of our algorithm on various indoor scenes and unseen
number of robots, when only trained with 9 indoor scenes.
