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Search and rescue with a team of heterogeneous mobile robots in unknown andlarge-scale underground environments requires high-precision localization andmapping. This crucial requirement is faced with many challenges in complex andperceptually-degraded subterranean environments, as the onboard perceptionsystem is required to operate in off-nominal conditions (poor visibility due todarkness and dust, rugged and muddy terrain, and the presence of self-similarand ambiguous scenes). In a disaster response scenario and in the absence ofprior information about the environment, robots must rely on noisy sensor dataand perform Simultaneous Localization and Mapping (SLAM) to build a 3D map ofthe environment and localize themselves and potential survivors. To that end,this paper reports on a multi-robot SLAM system developed by team CoSTAR in thecontext of the DARPA Subterranean Challenge. We extend our previous work, LAMP,by incorporating a single-robot front-end interface that is adaptable todifferent odometry sources and lidar configurations, a scalable multi-robotfront-end to support inter- and intra-robot loop closure detection for largescale environments and multi-robot teams, and a robust back-end equipped withan outlier-resilient pose graph optimization based on Graduated Non-Convexity.We provide a detailed ablation study on the multi-robot front-end and back-end,and assess the overall system performance in challenging real-world datasetscollected across mines, power plants, and caves in the United States. We alsorelease our multi-robot back-end datasets (and the corresponding ground truth),which can serve as challenging benchmarks for large-scale underground SLAM.

LAMP 2.0: A Robust Multi-Robot SLAM System for Operation in Challenging Large-Scale Underground Environments