# ROMAN: Open-Set Object Map Alignment for Robust View-Invariant Global Localization

arXiv: https://arxiv.org/abs/2410.08262 · HTML: https://mlsanity.com/paper/2410.08262/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Oct 2024.

## Built on by (most cited first, 10 of 10)

- [Language-Grounded Hierarchical Planning and Execution with Multi-Robot 3D Scene Graphs](https://mlsanity.com/paper/2506.07454/) (2025)
- [Bayesian Fields: Task-driven Open-Set Semantic Gaussian Splatting](https://mlsanity.com/paper/2503.05949/) (2025)
- [FreeOcc: Training-Free Embodied Open-Vocabulary Occupancy Prediction](https://mlsanity.com/paper/2604.28115/) (2026)
- [Think While You Map: Asynchronous Vision-Language Agents for Incremental 3D Scene Graphs](https://mlsanity.com/paper/2606.31471/) (2026)
- [LunarLoc: Segment-Based Global Localization on the Moon](https://mlsanity.com/paper/2506.16940/) (2025)
- [G2G: Exploiting Intra-Group Geometry for Inter-Group Pose Estimation](https://mlsanity.com/paper/2606.08284/) (2026)
- [HERCULES: An Open-Source Simulation Framework for Heterogeneous Multi-Robot SLAM, Collaborative Perception, and Exploration](https://mlsanity.com/paper/2606.22756/) (2026)
- [VISTA: Monocular Segmentation-Based Mapping for Appearance and View-Invariant Global Localization](https://mlsanity.com/paper/2507.11653/) (2025)
- [OpenSGA: Efficient 3D Scene Graph Alignment in the Open World](https://mlsanity.com/paper/2605.10484/) (2026)
- [VideoReloc: Long-Term Indoor Video Relocalization against a Kilobyte-Scale Semantic Scene Graph](https://mlsanity.com/paper/2609.21804/) (2026)

## Abstract (arXiv)

Global localization is a fundamental capability required for long-term and drift-free robot navigation. However, current methods fail to relocalize when faced with significantly different viewpoints. We present ROMAN (Robust Object Map Alignment Anywhere), a global localization method capable of localizing in challenging and diverse environments by creating and aligning maps of open-set and view-invariant objects. ROMAN formulates and solves a registration problem between object submaps using a unified graph-theoretic global data association approach with a novel incorporation of a gravity direction prior and object shape and semantic similarity. This work's open-set object mapping and information-rich object association algorithm enables global localization, even in instances when maps are created from robots traveling in opposite directions. Through a set of challenging global localization experiments in indoor, urban, and unstructured/forested environments, we demonstrate that ROMAN achieves higher relative pose estimation accuracy than other image-based pose estimation methods or segment-based registration methods. Additionally, we evaluate ROMAN as a loop closure module in large-scale multi-robot SLAM and show a 35% improvement in trajectory estimation error compared to standard SLAM systems using visual features for loop closures. Code and videos can be found at https://acl.mit.edu/roman.