Metraipolis › archive › dc_ee3be93033bb

Cross-View Sequential Visual Localization with Spatio-Temporal Context Modeling for Autonomous Driving — arXiv:2608.10660

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.10660
License (as archived): CC0-1.0
Archived: 2026-10-19T06:09:20.582Z
SHA-256 of the archived text: 8f94a8f6e43e314c6835b084f41a4684637566e7b14079eca866a3300f0dd826
Stable URL of this page: https://metraipolis.com/documento/dc_ee3be93033bb

How to cite

Cross-View Sequential Visual Localization with Spatio-Temporal Context Modeling for Autonomous Driving — arXiv:2608.10660 — arXiv (en), https://arxiv.org/abs/2608.10660 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ee3be93033bb (Metraipolis archive dc_ee3be93033bb, sha256 8f94a8f6e43e314c6835b084f41a4684637566e7b14079eca866a3300f0dd826)

BibTeX

@misc{dcee3be93033bb,
  title = {Cross-View Sequential Visual Localization with Spatio-Temporal Context Modeling for Autonomous Driving — arXiv:2608.10660},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.10660},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ee3be93033bb; sha256 8f94a8f6e43e314c6835b084f41a4684637566e7b14079eca866a3300f0dd826},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.10660. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-12 in cs.AI).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2608.10660 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-19T05:57:28.391Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-08-12?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).