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 robust global localization method capable of localizing in challenging and diverse environments based on creating and aligning maps of open-set and view-invariant objects. To address localization difficulties caused by feature-sparse or perceptually aliased environments, ROMAN formulates and solves a registration problem between object submaps using a unified graph-theoretic global data association approach that simultaneously accounts for object shape and semantic similarities and a prior on gravity direction. Through a set of challenging large-scale multi-robot or multi-session SLAM experiments in indoor, urban and unstructured/forested environments, we demonstrate that ROMAN achieves a maximum recall 36% higher than other object-based map alignment methods and an absolute trajectory error that is 37% lower than using visual features for loop closures.
@article{peterson2024roman,
author = {Peterson, Mason and Jia, Yi Xuan and Tian, Yulun and Thomas, Annika and How, Jonathan},
title = {ROMAN: Open-Set Object Map Alignment for Robust View-Invariant Global Localization},
journal = {arXiv preprint arXiv:2410.08262},
year = {2024},
}