外观
论文arXiv 2608.106602 个月前
面向自主驾驶的基于时空上下文建模的跨视角序列视觉定位
原文链接：Cross-View Sequential Visual Localization with Spatio-Temporal Context Modeling for Autonomous Driving（arXiv 2608.10660 · 发布日期：2026-08-11）
Cross-View Sequential Visual Localization with Spatio-Temporal Context Modeling for Autonomous Driving
针对自动驾驶跨视角定位时序信息不足，提出循环跨帧时序上下文框架，并在CVIS、KITTI-CVL和真实车辆实验验证。
核心要点
- 现有跨视角定位多逐帧处理，时序信息利用不足。
- 循环跨帧模块聚合历史上下文，增强当前帧粗粒度地面特征。
- 增强特征用于卫星候选区域分类，层级细粒度特征估计局部偏移。
- 在CVIS、KITTI-CVL及真实道路车辆实验中提升定位精度。
验证范围与局限：验证包括CVIS、KITTI-CVL直接迁移/微调，以及真实车辆零样野外实验。
英文摘要 (Abstract)
Continuous and reliable localization is essential for autonomous driving. Cross-view visual localization matches ground images with satellite maps, providing complementary localization cues for pipelines that depend on Global Navigation Satellite System (GNSS) signals and high-definition (HD) maps. Most existing cross-view visual localization methods process each frame independently, leaving temporal information underused and limiting accuracy under dynamic occlusion, illumination variation, and repetitive textures. This study proposes a temporal-context-enhanced framework for cross-view sequence visual localization. The proposed recurrent cross-frame module aggregates historical context from the previous state to enhance the coarse ground feature of each current frame. These enhanced features facilitate satellite candidate-region classification, while hierarchical fine-grained features enable precise local offset estimation. On the CVIS dataset, the proposed method reduces mean localization error from 3.80 m to 1.57 m and increases R@1 m from 8.14% to 40.22%. Direct transfer to KITTI-CVL achieves a mean error of 2.61 m, with target-domain fine-tuning further reducing the mean error to 2.27 m. Zero-shot field experiments on a real-world vehicle achieve a mean error of 2.84 m and R@5 m of 96.86%. These results demonstrate that temporal context enhancement significantly improves cross-view localization accuracy and supports robust deployment on public benchmarks and real-world roads.
作者：Jiaping Wang, Shaobo Li, Zhen Wang
来源
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