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