AI 中文总结
针对特征稀疏场景下跨视图地理定位失效问题，提出SeqLoc序列聚合机制，在CV-FSS等基准上使位置与方向召回率提升超50%，相关资源将公开。
AI 中文摘要
结合OpenStreetMap（OSM）的跨视图地理定位（CVGL）在结构丰富的城市环境中表现良好，但在农村道路等特征稀疏场景中失效。为研究该失效模式，本文引入CV-FSS基准，其将来自五个农村区域的序列全景图与对齐的OSM地图配对，单帧方法在该基准上性能大幅下降。随后提出SeqLoc，一种在线测试时序列聚合机制，递归维护包含三个关键组件的对数置信体积：（1）熵调不确定性（ETU），通过归一化熵对每个传入的位姿似然体积进行调参；（2）地图引导重定位（MGR），将地图形状的恢复分布混入置信度，使被抑制的真实位姿得以恢复；（3）峰值锚定平滑（PAS），以亚网格精度推导最终位姿。在CV-FSS和CV-RHO上的大量实验表明，SeqLoc大幅优于单帧定位，将位置和方向召回率均提升50%以上。该基准和源代码将公开提供。
英文摘要
Cross-View Geo-Localization (CVGL) with OpenStreetMap (OSM) performs well in structure-rich urban environments but collapses in feature-sparse scenes such as rural roads. To study this failure mode, in this work, we introduce CV-FSS, a benchmark that pairs sequential panoramas from five rural regions with aligned OSM maps, on which single-frame methods degrade drastically. We then propose SeqLoc, an online test-time sequence aggregation mechanism that recursively maintains a log-belief volume with three key components: (1) Entropy-Tempered Uncertainty (ETU) tempers each incoming pose likelihood volume by its normalized entropy; (2) Map-Guided Relocalization (MGR) mixes a map-shaped recovery distribution into the belief so that a suppressed true pose can recover; (3) Peak-Anchored Smoothing (PAS) derives the final pose at sub-grid precision. Extensive experiments on CV-FSS and CV-RHO demonstrate that SeqLoc outperforms single-frame localization by a large margin, improving both position and orientation recall by over 50%. The benchmark and source code are publicly available at https://zhengjunwei.com/publications/SeqLoc/SeqLoc.html.
CommentsProject page: https://zhengjunwei.com/publications/SeqLoc/SeqLoc.html
