  Correspondence identification (CoID) is an essential component for
collaborative perception in multi-robot systems, such as connected autonomous
vehicles. The goal of CoID is to identify the correspondence of objects
observed by multiple robots in their own field of view in order for robots to
consistently refer to the same objects. CoID is challenging due to perceptual
aliasing, object non-covisibility, and noisy sensing. In this paper, we
introduce a novel deep masked graph matching approach to enable CoID and
address the challenges. Our approach formulates CoID as a graph matching
problem and we design a masked neural network to integrate the multimodal
visual, spatial, and GPS information to perform CoID. In addition, we design a
new technique to explicitly address object non-covisibility caused by occlusion
and the vehicle's limited field of view. We evaluate our approach in a variety
of street environments using a high-fidelity simulation that integrates the
CARLA and SUMO simulators. The experimental results show that our approach
outperforms the previous approaches and achieves state-of-the-art CoID
performance in connected autonomous driving applications. Our work is available
at: https://github.com/gaopeng5/DMGM.git.
