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One-shot LiDAR localization refers to the ability to estimate the robot posefrom one single point cloud, which yields significant advantages ininitialization and relocalization processes. In the point cloud domain, thetopic has been extensively studied as a global descriptor retrieval (i.e., loopclosure detection) and pose refinement (i.e., point cloud registration) problemboth in isolation or combined. However, few have explicitly considered therelationship between candidate retrieval and correspondence generation in poseestimation, leaving them brittle to substructure ambiguities. To this end, wepropose a hierarchical one-shot localization algorithm called Outram thatleverages substructures of 3D scene graphs for locally consistentcorrespondence searching and global substructure-wise outlier pruning. Such ahierarchical process couples the feature retrieval and the correspondenceextraction to resolve the substructure ambiguities by conducting alocal-to-global consistency refinement. We demonstrate the capability of Outramin a variety of scenarios in multiple large-scale outdoor datasets. Ourimplementation is open-sourced: https://github.com/Pamphlett/Outram.

Outram: One-shot Global Localization via Triangulated Scene Graph and Global Outlier Pruning