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arXiv · 2606.06312

Meridian: Metric-Semantic Primitive Matching for Cross-View Geo-Localization Beyond Urban Environments

Mason Peterson·Qingyuan Li·Yixuan Jia·Fernando Cladera·Carlos Nieto-Granda·Camillo Jose Taylor·Jonathan P. How
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Abstract

Successful robot automation requires accurate global localization to support repeatability, task planning, goal specification, and safe operation. However, reliable localization in GNSS-denied environments remains an open problem. Overhead aerial imagery offers a promising solution, but existing approaches primarily target structured urban environments and have been rarely demonstrated in unstructured natural terrain. Limitations of the state-of-the-art include a reliance on models trained for specific environments, as well as difficulty handling repetitive geometries and featureless landscapes commonly found in natural outdoor areas. To overcome these challenges, we present Meridian, a method for matching high-level metric-semantic primitives across aerial images and ground robot RGB-D camera data that achieves accurate global localization and generalizes well across diverse environments, all without any training or algorithmic fine-tuning on area-specific data. We formulate novel consistency metrics to estimate a distribution over robot submap poses and to reject outlier hypotheses in a robust pose graph optimization step for accurate robot trajectory estimation. We demonstrate that our algorithm can localize a ground robot across a wide variety of environments, including an autonomous driving dataset, a park and campus area, and a wilderness camp, with an average optimized trajectory error of 2.4 m over 19 km of ground traversal.

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Mason Peterson, Qingyuan Li, Yixuan Jia, Fernando Cladera, Carlos Nieto-Granda, Camillo Jose Taylor, Jonathan P. How. 2026-06-04. Meridian: Metric-Semantic Primitive Matching for Cross-View Geo-Localization Beyond Urban Environments. https://arxiv.org/abs/2606.06312

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