SeqLoc通过序列信息提升农村地区定位精度，解决单帧失效问题。
SeqLoc: Beyond the Single Frame for Cross-View Geo-Localization in Feature-Sparse Scenes
- 引入序列融合机制，动态更新置信体积以增强定位鲁棒性。
- 在农村场景下位置与朝向召回率均提升超50%。
- 适合低纹理环境下的自动驾驶定位研究者使用。
基于开放街图（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性能急剧下降。为研究此失效模式，本文提出CV-FSS基准，包含五个农村区域的连续全景图像及其对齐的OSM地图，单帧方法在此基准上表现大幅下滑。为此，我们提出SeqLoc，一种在线测试时的序列聚合机制，通过三个关键组件递归维护日志置信体积：(1) 熵加权不确定性（ETU）根据每帧姿态似然体积的归一化熵进行加权；(2) 地图引导重定位（MGR）将地图形状的恢复分布融入置信体，使被压制的真实姿态得以恢复；(3) 峰锚平滑（PAS）实现亚像素级姿态估计。在CV-FSS和CV-RHO上的大量实验表明，SeqLoc显著优于单帧定位方法，位置与朝向召回率均提升超过50%。相关基准与源码已公开于https://zhengjunwei.com/publications/SeqLoc/SeqLoc.html。
原文摘要 · Abstract (English)
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.
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。