Correspondence identification is essential for multi-robot collaborative perception, which aims to identify the same objects in order to ensure consistent references of the objects by a group of robots/agents in their own fields of view. Although recent deep learning methods have shown encouraging performance on correspondence identification, they suffer from two shortcomings, including the inability to address non-covisibility in collaborative perception that is caused by occlusion and limited fields of view of the agents, and the inability to quantify and reduce uncertainty to improve correspondence identification. To address both issues, we propose a novel uncertainty-aware deep graph matching method for correspondence identification in collaborative perception. Our method formulates correspondence identification as a deep graph matching problem, which identifies correspondences based upon graph representations that are constructed from robot observations. We propose new deep graph matching networks in the Bayesian framework to explicitly quantify uncertainty in identified correspondences. In addition, we design novel loss functions in order to explicitly reduce correspondence uncertainty and perceptual non-covisibility during learning. We evaluate our method in the robotics applications of collaborative assembly and multi-robot coordination using high-fidelity simulations and physical robots. Experiments have validated that, by addressing uncertainty and non-covisibility, our proposed approach achieves the state-of-the-art performance of correspondence identification.
