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

• URL: http://arxiv.org/abs/2606.06312v1
• Date: Thu, 04 Jun 2026 15:52:40 GMT
• Title: Meridian: Metric-Semantic Primitive Matching for Cross-View Geo-Localization Beyond Urban Environments
• Abstract summary: Successful robot automation requires accurate global localization to support repeatability, task planning, goal specification, and safe operation.<n>We present Meridian, a method for matching high-level metric-semantic primitives across aerial images and ground robot RGB-D camera data.<n>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.
• Score: 47.53030013880309
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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