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标题: "Cross-View Sequential Visual Localization with Spatio-Temporal Context Modeling for Autonomous Driving"
原文链接: "https://arxiv.org/abs/2608.10660"
发布日期: "2026-08-11"
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去重键: "https://arxiv.org/abs/2608.10660"
---

## Computer Science > Computer Vision and Pattern Recognition

## Title:Cross-View Sequential Visual Localization with Spatio-Temporal Context Modeling for Autonomous Driving

Authors:[Jiaping Wang](https://arxiv.org/search/cs?searchtype=author&query=Wang,+J), [Shaobo Li](https://arxiv.org/search/cs?searchtype=author&query=Li,+S), [Zhen Wang](https://arxiv.org/search/cs?searchtype=author&query=Wang,+Z)

[View PDF](https://arxiv.org/pdf/2608.10660)

> 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.

| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| --- | --- |
| Cite as: | [arXiv:2608.10660](https://arxiv.org/abs/2608.10660) \[cs.CV\] |
|  | (or [arXiv:2608.10660v1](https://arxiv.org/abs/2608.10660v1) \[cs.CV\] for this version) |
|  | [https://doi.org/10.48550/arXiv.2608.10660](https://doi.org/10.48550/arXiv.2608.10660) |

## Submission history

From: Zhen Wang \[[view email](https://arxiv.org/show-email/4d9d5222/2608.10660)\]  
**\[v1\]** Tue, 11 Aug 2026 08:44:42 UTC (1,721 KB)

[Which authors of this paper are endorsers?](https://arxiv.org/auth/show-endorsers/2608.10660) | Disable MathJax ([What is MathJax?](https://info.arxiv.org/help/mathjax.html))
