DARE-SLAM: Degeneracy-Aware and Resilient Loop Closing in Perceptually-Degraded Environments

Abstract

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 that can be used for reliable navigation. 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 large-scale GPS-denied and perceptually-degraded environments such as lava tubes, caves, and mines, where current methods have inadequate performance. The first contribution of this paper is a degeneracy-aware lidar-based SLAM front-end to determine the level of geometric degeneracy in an unknown environment. Using this crucial capability, unobservable areas in the environment are determined and excluded from the search for loop closures to avoid spurious loop closures that can lead to distortions of the map. The second contribution of this paper is a drift-resilient loop closing pipeline that exploits the salient 2D and 3D features extracted from lidar point cloud data to enable a robust multi-stage loop closing capability. A key advantage of proposed method is that it is pose-invariant, and thus, it is unaffected by drift and accumulation of errors in the estimated robot trajectory. We present extensive evaluation and analysis of performance and robustness, and provide comparison of localization and mapping results with the state-of-the-art methods in a variety of extreme and perceptually-degraded underground mines across the United States.