{"ID":3083642,"CreatedAt":"2026-06-05T06:46:15.197025399Z","UpdatedAt":"2026-06-07T03:21:39.539466367Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.06312","arxiv_id":"2606.06312","title":"Meridian: Metric-Semantic Primitive Matching for Cross-View Geo-Localization Beyond Urban Environments","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.","short_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 ta...","url_abs":"https://arxiv.org/abs/2606.06312","url_pdf":"https://arxiv.org/pdf/2606.06312v1","authors":"[\"Mason Peterson\",\"Qingyuan Li\",\"Yixuan Jia\",\"Fernando Cladera\",\"Carlos Nieto-Granda\",\"Camillo Jose Taylor\",\"Jonathan P. How\"]","published":"2026-06-04T15:52:40Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[]","has_code":false}