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

arXiv: https://arxiv.org/abs/2309.08914 · HTML: https://mlsanity.com/paper/2309.08914/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2023.

## Built on by (most cited first, 9 of 9)

- [UAVScenes: A Multi-Modal Dataset for UAVs](https://mlsanity.com/paper/2507.22412/) (2025)
- [ULOC: Learning to Localize in Complex Large-Scale Environments with Ultra-Wideband Ranges](https://mlsanity.com/paper/2409.11122/) (2024)
- [REGRACE: A Robust and Efficient Graph-based Re-localization Algorithm using Consistency Evaluation](https://mlsanity.com/paper/2503.03599/) (2025)
- [Interactive Test-Time Adaptation with Reliable Spatial-Temporal Voxels for Multi-Modal Segmentation](https://mlsanity.com/paper/2403.06461/) (2024)
- [SGBA: Semantic Gaussian Mixture Model-Based LiDAR Bundle Adjustment](https://mlsanity.com/paper/2410.01618/) (2024)
- [NVP-HRI: Zero Shot Natural Voice and Posture-based Human-Robot Interaction via Large Language Model](https://mlsanity.com/paper/2503.09335/) (2025)
- [RE-TRIP : Reflectivity Instance Augmented Triangle Descriptor for 3D Place Recognition](https://mlsanity.com/paper/2505.16165/) (2025)
- [Gaussian Semantic Field for One-shot LiDAR Global Localization](https://mlsanity.com/paper/2510.12101/) (2025)
- [VideoReloc: Long-Term Indoor Video Relocalization against a Kilobyte-Scale Semantic Scene Graph](https://mlsanity.com/paper/2609.21804/) (2026)

## Abstract (arXiv)

One-shot LiDAR localization refers to the ability to estimate the robot pose from one single point cloud, which yields significant advantages in initialization and relocalization processes. In the point cloud domain, the topic has been extensively studied as a global descriptor retrieval (i.e., loop closure detection) and pose refinement (i.e., point cloud registration) problem both in isolation or combined. However, few have explicitly considered the relationship between candidate retrieval and correspondence generation in pose estimation, leaving them brittle to substructure ambiguities. To this end, we propose a hierarchical one-shot localization algorithm called Outram that leverages substructures of 3D scene graphs for locally consistent correspondence searching and global substructure-wise outlier pruning. Such a hierarchical process couples the feature retrieval and the correspondence extraction to resolve the substructure ambiguities by conducting a local-to-global consistency refinement. We demonstrate the capability of Outram in a variety of scenarios in multiple large-scale outdoor datasets. Our implementation is open-sourced: https://github.com/Pamphlett/Outram.