DARE-SLAM: Degeneracy-Aware and Resilient Loop Closing in Perceptually-Degraded Environments
This work aims to improve the robustness and accuracy of SLAM systems for autonomous robots operating in challenging, GPS-denied, and perceptually-degraded subterranean environments, where current methods perform inadequately.
This paper addresses the challenge of loop closing in perceptually-degraded subterranean environments for simultaneous localization and mapping (SLAM). It introduces a degeneracy-aware and drift-resilient method to improve place recognition and resolve 3D location ambiguities, aiming to enhance mapping consistency in complex and ambiguous settings like lava tubes, caves, and mines.
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.