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

TL;DR

This paper introduces DARE-SLAM, a novel loop closing method designed to enhance place recognition and resolve 3D ambiguities in challenging subterranean environments, improving autonomous robot mapping and navigation.

Contribution

The paper presents a degeneracy-aware, drift-resilient loop closing approach specifically tailored for SLAM in perceptually-degraded, large-scale subterranean environments, addressing limitations of existing methods.

Findings

Improved loop closure detection accuracy in subterranean environments

Enhanced map consistency and reduced drift in SLAM

Effective handling of 3D location ambiguities

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 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.