Distributed Multi-Robot Object SLAM using Variational Inference and Consensus Averaging

This paper presents a distributed approach for multi-robot simultaneous localization and mapping (SLAM) that formulates the problem as a variational inference problem over a communication graph. The method imposes a consensus constraint on the object landmarks maintained by different robots to ensure agreement on a common map, and uses a distributed mirror descent algorithm with a regularization term to enforce this consensus.