Correspondence Identification in Collaborative Robot Perception through Maximin Hypergraph Matching

摘要

Correspondence identification is an essential problem for collaborative multi-robot perception, with the objective of deciding the correspondence of objects that are observed in the field of view of each robot. In this paper, we introduce a novel maximin hypergraph matching approach that formulates correspondence identification as a hypergraph matching problem. The proposed approach incorporates both spatial relationships and appearance features of objects to improve representation capabilities. It also integrates the maximin theorem to optimize the worst-case scenario in order to address distractions caused by non-covisible objects. In addition, we design an optimization algorithm to address the formulated non-convex non-continuous optimization problem. We evaluate our approach and compare it with seven previous techniques in two application scenarios, including multi-robot coordination on real robots and connected autonomous driving in simulations. Experimental results have validated the effectiveness of our approach in identifying object correspondence from partially overlapped views in collaborative perception, and have shown that the proposed maximin hypergraph matching approach outperforms previous techniques and obtains state-of-the-art performance.