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Collaborative object localization aims to collaboratively estimate locationsof objects observed from multiple views or perspectives, which is a criticalability for multi-agent systems such as connected vehicles. To enablecollaborative localization, several model-based state estimation andlearning-based localization methods have been developed. Given theirencouraging performance, model-based state estimation often lacks the abilityto model the complex relationships among multiple objects, while learning-basedmethods are typically not able to fuse the observations from an arbitrarynumber of views and cannot well model uncertainty. In this paper, we introducea novel spatiotemporal graph filter approach that integrates graph learning andmodel-based estimation to perform multi-view sensor fusion for collaborativeobject localization. Our approach models complex object relationships using anew spatiotemporal graph representation and fuses multi-view observations in aBayesian fashion to improve location estimation under uncertainty. We evaluateour approach in the applications of connected autonomous driving and multiplepedestrian localization. Experimental results show that our approachoutperforms previous techniques and achieves the state-of-the-art performanceon collaboration localization.

Multi-view Sensor Fusion by Integrating Model-based Estimation and Graph Learning for Collaborative Object Localization