用全景图和地图实现更鲁棒的精准定位。
RHO: Robust Holistic OSM-Based Metric Cross-View Geo-Localization
- 用全景图与开放街景地图构建跨视角定位模型。
- 在270万张不同光照天气图像上提升定位准确率20%。
- 适合需要高精度室外定位的研究与应用者。
度量跨视图地理定位（MCVGL）旨在通过匹配地面与卫星图像来估计相机的3自由度姿态（位置与朝向）。本文研究使用全景图与开放街景地图（OSM）进行鲁棒的度量跨视图地理定位。为此，我们构建了一个大规模基准数据集CV-RHO，包含超过270万张在不同天气、光照条件及传感器噪声下的图像。同时提出名为RHO的双分支Pin-Pan架构模型，引入分块去畸变合并（SUM）模块解决全景图畸变问题，并设计位置-朝向融合（POF）机制提升定位精度。大量实验验证了CV-RHO数据集的价值和RHO模型的有效性，在性能上相比最先进基线最高提升20%。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。