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An essential task for a multi-robot system is generating a commonunderstanding of the environment and relative poses between robots. Cooperativetasks can be executed only when a vehicle has knowledge of its own state andthe states of the team members. However, this has primarily been achieved withdirect rendezvous between underwater robots, via inter-robot ranging. Wepropose a novel distributed multi-robot simultaneous localization and mapping(SLAM) framework for underwater robots using imaging sonar-based perception. Bypassing only scene descriptors between robots, we do not need to pass rawsensor data unless there is a likelihood of inter-robot loop closure. Weutilize pairwise consistent measurement set maximization (PCM), making oursystem robust to erroneous loop closures. The functionality of our system isdemonstrated using two real-world datasets, one with three robots and anotherwith two robots. We show that our system effectively estimates the trajectoriesof the multi-robot system and keeps the bandwidth requirements of inter-robotcommunication low. To our knowledge, this paper describes the first instance ofmulti-robot SLAM using real imaging sonar data (which we implement offline,using simulated communication). Code link:https://github.com/jake3991/DRACo-SLAM.

DRACo-SLAM: Distributed Robust Acoustic Communication-efficient SLAM for Imaging Sonar Equipped Underwater Robot Teams