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To achieve collaborative tasks, robots in a team need to have a sharedunderstanding of the environment and their location within it. DistributedSimultaneous Localization and Mapping (SLAM) offers a practical solution tolocalize the robots without relying on an external positioning system (e.g.GPS) and with minimal information exchange. Unfortunately, current distributedSLAM systems are vulnerable to perception outliers and therefore tend to usevery conservative parameters for inter-robot place recognition. However, beingtoo conservative comes at the cost of rejecting many valid loop closurecandidates, which results in less accurate trajectory estimates. This paperintroduces DOOR-SLAM, a fully distributed SLAM system with an outlier rejectionmechanism that can work with less conservative parameters. DOOR-SLAM is basedon peer-to-peer communication and does not require full connectivity among therobots. DOOR-SLAM includes two key modules: a pose graph optimizer combinedwith a distributed pairwise consistent measurement set maximization algorithmto reject spurious inter-robot loop closures; and a distributed SLAM front-endthat detects inter-robot loop closures without exchanging raw sensor data. Thesystem has been evaluated in simulations, benchmarking datasets, and fieldexperiments, including tests in GPS-denied subterranean environments. DOOR-SLAMproduces more inter-robot loop closures, successfully rejects outliers, andresults in accurate trajectory estimates, while requiring low communicationbandwidth. Full source code is available athttps://github.com/MISTLab/DOOR-SLAM.git.

DOOR-SLAM: Distributed, Online, and Outlier Resilient SLAM for Robotic Teams