Robotics: Science and Systems XXI
ROMAN: Open-Set Object Map Alignment for Robust View-Invariant Global Localization
Mason Peterson, Yixuan Jia, Yulun Tian, Annika Thomas, Jonathan P. HowAbstract:
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.
Bibtex:
@INPROCEEDINGS{PetersonM-RSS-25, AUTHOR = {Mason Peterson AND Yixuan Jia AND Yulun Tian AND Annika Thomas AND Jonathan P. How}, TITLE = {{ROMAN: Open-Set Object Map Alignment for Robust View-Invariant Global Localization}}, BOOKTITLE = {Proceedings of Robotics: Science and Systems}, YEAR = {2025}, ADDRESS = {LosAngeles, CA, USA}, MONTH = {June}, DOI = {10.15607/RSS.2025.XXI.029} }