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DARE-SLAM: Degeneracy-Aware and Resilient Loop Closing in Perceptually-Degraded Environments
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DARE-SLAM: Degeneracy-Aware and Resilient Loop Closing in Perceptually-Degraded Environments
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Paper
DARE-SLAM: Degeneracy-Aware and Resilient Loop Closing in Perceptually-Degraded Environments
2021
Overview
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.
Publisher
Cornell University Library, arXiv.org
