Meridian: 超越城市环境的跨视角地理定位的度量-语义基元匹配
Meridian: Metric-Semantic Primitive Matching for Cross-View Geo-Localization Beyond Urban Environments
AI总结：
提出Meridian方法，通过匹配航拍图像与地面机器人RGB-D数据中的高层度量-语义基元，无需特定区域训练即可实现跨多种环境的全局定位，平均轨迹误差2.4米。
摘要：
成功的机器人自动化需要准确的全局定位以支持可重复性、任务规划、目标指定和安全操作。然而，在GNSS受限环境中的可靠定位仍然是一个开放问题。高空航拍图像提供了一种有前景的解决方案，但现有方法主要针对结构化城市环境，很少在非结构化自然地形中得到验证。现有技术的局限性包括依赖针对特定环境训练的模型，以及在自然户外区域常见的重复几何和无特征景观中难以处理。为克服这些挑战，我们提出了Meridian，一种在航拍图像和地面机器人RGB-D相机数据之间匹配高层度量-语义基元的方法，实现了准确的全局定位，并在多样环境中具有良好的泛化能力，无需任何针对特定区域数据的训练或算法微调。我们提出了新颖的一致性度量来估计机器人子图位姿的分布，并在鲁棒的位姿图优化步骤中剔除异常假设，以实现准确的机器人轨迹估计。我们证明了我们的算法可以在多种环境中定位地面机器人，包括自动驾驶数据集、公园和校园区域以及荒野营地，在19公里的地面遍历中平均优化轨迹误差为2.4米。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
