Decentralized cooperative simultaneous localization and mapping for dynamic and sparse robot networks

Communication among robots is key to performance in cooperative multi-robot systems. In practice, communication connections for information exchange between all robots are not always guaranteed, which adds difficulty to state estimation. This paper examines the decentralized cooperative simultaneous localization and mapping (SLAM) problem under a sparsely-communicating and dynamic network. We mathematically prove how the centralized-equivalent estimate can be obtained by all robots in the network in a decentralized manner. Furthermore, a robot only needs to consider its own knowledge of the network topology to detect when the centralized-equivalent estimate is obtainable. Our approach is validated through more than 250 minutes of experiments using a team of real robots, with accurate groundtruth data of all robots and landmark features.