  Enabling fully autonomous robots capable of navigating and exploring
large-scale, unknown and complex environments has been at the core of robotics
research for several decades. A key requirement in autonomous exploration is
building accurate and consistent maps of the unknown environment that can be
used for reliable navigation. Loop closure detection, the ability to assert
that a robot has returned to a previously visited location, is crucial for
consistent mapping as it reduces the drift caused by error accumulation in the
estimated robot trajectory. Moreover, in multi-robot systems, loop closures
enable merging local maps obtained by a team of robots into a consistent global
map of the environment. In this paper, we present a degeneracy-aware and
drift-resilient loop closing method to improve place recognition and resolve 3D
location ambiguities for simultaneous localization and mapping (SLAM) in
GPS-denied, large-scale and perceptually-degraded environments. More
specifically, we focus on SLAM in subterranean environments (e.g., lava tubes,
caves, and mines) that represent examples of complex and ambiguous environments
where current methods have inadequate performance.
