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

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 atthis https URL.

@article{peterson2025_2410.08262,
  title={ ROMAN: Open-Set Object Map Alignment for Robust View-Invariant Global Localization },
  author={ Mason B. Peterson and Yixuan Jia and Yulun Tian and Annika Thomas and Jonathan P. How },
  journal={arXiv preprint arXiv:2410.08262},
  year={ 2025 }
}