Outdoor Global Localization via Robust Registration of 3D Open-Set Segments is a research project aimed at achieving efficient and accurate global localiztion using sparse segments.
- Code: GitHub Repository
- Paper: Research Paper
Global localization is a fundamental capability enabling long-term and drift-free robot navigation. This paper presents an outdoor global localization method based on robust registration of open-set segment maps. Our method detects and tracks segments using open-set image segmentation models that enable direct generalization to unseen environments. To perform global localization under the high outlier regimes that are typical of natural/outdoor environments, we formulate a registration problem between small submaps of 3D segments and solve for the correspondences using a graph-theoretic global data association approach. Further, to guide registration in highly noisy or ambiguous scenarios, we propose novel ways of incorporating additional information (e.g., segment attributes and known gravity direction) within the global data association formulation. The proposed method is evaluated on outdoor datasets recorded by multiple robots and shown to outperform existing methods in terms of precision-recall metrics and localization accuracy.
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