ULSM: Underground Localization and Semantic Mapping with Salient Region Loop Closure under Perceptually-Degraded Environment

Abstract

Simultaneous Localization and Mapping (SLAM) has greatly assisted in exploring perceptually-degraded underground environments, such as human-made tunnels, mine tunnels, and caves. However, the recurring sensor failures and spurious loop closures in these scenes bring significant challenges to applying SLAM. This paper proposes an architecture for underground localization and semantic mapping (ULSM) that promotes the robustness of odometry estimation and map-building. In this architecture, a two-stage robust motion compensation method is proposed to adapt to sensor-failure situations. The proposed salient region loop closure detection contributes to avoiding spurious loop closures. Meanwhile, the 2D pose as the initial value for point cloud registration is estimated without additional input. We also design a multi-robot cooperative mapping scheme based on descriptors of the salient region. Extensive experiments are conducted on datasets collected in the Tunnel Circuit of DARPA Subterranean Challenge.