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Simultaneous Localization and Mapping (SLAM) in large-scale, unknown, andcomplex subterranean environments is a challenging problem. Sensors mustoperate in off-nominal conditions; uneven and slippery terrains make wheelodometry inaccurate, while long corridors without salient features makeexteroceptive sensing ambiguous and prone to drift; finally, spurious loopclosures that are frequent in environments with repetitive appearance, such astunnels and mines, could result in a significant distortion of the entire map.These challenges are in stark contrast with the need to build highly-accurate3D maps to support a wide variety of applications, ranging from disasterresponse to the exploration of underground extraterrestrial worlds. This paperreports on the implementation and testing of a lidar-based multi-robot SLAMsystem developed in the context of the DARPA Subterranean Challenge. We presenta system architecture to enhance subterranean operation, including an accuratelidar-based front-end, and a flexible and robust back-end that automaticallyrejects outlying loop closures. We present an extensive evaluation inlarge-scale, challenging subterranean environments, including the resultsobtained in the Tunnel Circuit of the DARPA Subterranean Challenge. Finally, wediscuss potential improvements, limitations of the state of the art, and futureresearch directions.

LAMP: Large-Scale Autonomous Mapping and Positioning for Exploration of Perceptually-Degraded Subterranean Environments