Regularized Graph Matching for Correspondence Identification under Uncertainty in Collaborative Perception

Abstract

Correspondence identification is a critical capability for multi-robot collaborative perception, which allows a group of robots to consistently refer to the same objects in their own fields of view. Correspondence identification is a challenging problem, especially caused by non-covisible objects that cannot be observed by all robots and the uncertainty in robot perception, which have not been well studied yet in collaborative perception. In this work, we propose a principled approach of regularized graph matching that addresses perception uncertainties and non-covisible objects in a unified mathematical framework to perform correspondence identification in collaborative perception. Our method formulates correspondence identification as a graph matching problem in the regularized constrained optimization framework. We introduce a regularization term to explicitly address perception uncertainties by penalizing the object correspondences with a high uncertainty. We also design a second regularization term to explicitly address non-covisible objects by penalizing the correspondences built by the non-covisible objects. The formulated constrained optimization problem is difficulty to solve, because it is not convex and it contains regularization terms. Thus, we develop a new sampling-based algorithm to solve our formulated regularized constrained optimization problem. We evaluate our approach in the scenarios of connected autonomous driving and multi-robot coordination in simulations and using real robots. Experimental results show that our method is able to address correspondence identification under uncertainty and non-covisibility, and it outperforms the previous techniques and achieves the state-of-the-art performance.