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

摘要

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 openset 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 segmentbased 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.