RHO：基于OpenStreetMap的鲁棒整体度量跨视角地理定位
RHO: Robust Holistic OSM-Based Metric Cross-View Geo-Localization
提出基于全景图与OSM的度量跨视角地理定位新设定，并设计Split-Undistort-Merge模块处理全景畸变、Position-Orientation Fusion机制融合位置与朝向，为视觉定位中全景畸变校正与多模态融合提供可复用技术。
原文依据 A Split-Undistort-Merge (SUM) module is introduced to address the panoramic distortion, and a Position-Orientation Fusion (POF) mechanism is designed to enhance the localization accuracy.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论度量跨视角地理定位（MCVGL）旨在通过匹配地面与卫星图像来估计3自由度相机位姿（位置与朝向）。本文不采用针孔与卫星图像，而是研究使用整体全景图与OpenStreetMap（OSM）的鲁棒MCVGL。为此，我们建立了一个大规模MCVGL基准数据集CV-RHO，包含超过270万张图像，覆盖不同天气与光照条件以及传感器噪声。此外，我们提出名为RHO的模型，采用双分支Pin-Pan架构进行精确视觉定位。引入Split-Undistort-Merge（SUM）模块以解决全景畸变问题，并设计Position-Orientation Fusion（POF）机制以提升定位精度。大量实验证明了CV-RHO数据集的价值与RHO模型的有效性，与最优基线相比性能提升高达20%。项目页面：https://github.com/InSAI-Lab/RHO。
英文原文摘要 985 字符
Metric Cross-View Geo-Localization (MCVGL) aims to estimate the 3-DoF camera pose (position and heading) by matching ground and satellite images. In this work, instead of pinhole and satellite images, we study robust MCVGL using holistic panoramas and OpenStreetMap (OSM). To this end, we establish a large-scale MCVGL benchmark dataset, CV-RHO, with over 2.7M images under different weather and lighting conditions, as well as sensor noise. Furthermore, we propose a model termed RHO with a two-branch Pin-Pan architecture for accurate visual localization. A Split-Undistort-Merge (SUM) module is introduced to address the panoramic distortion, and a Position-Orientation Fusion (POF) mechanism is designed to enhance the localization accuracy. Extensive experiments prove the value of our CV-RHO dataset and the effectiveness of the RHO model, with a significant performance gain up to 20% compared with the state-of-the-art baselines. Project page: https://github.com/InSAI-Lab/RHO.