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SP-Guide: Enhancing Aerial Vision-Language Navigation with Spatial Priors — arXiv:2610.32697

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.32697
License (as archived): CC0-1.0
Archived: 2026-10-26T19:52:34.731Z
SHA-256 of the archived text: a4c5becc87187c7e2455085c4304c586d0b001fbccd30968bc6e78c9e72b5cca
Stable URL of this page: https://metraipolis.com/documento/dc_2e8039394251

How to cite

SP-Guide: Enhancing Aerial Vision-Language Navigation with Spatial Priors — arXiv:2610.32697 — arXiv (en), https://arxiv.org/abs/2610.32697 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_2e8039394251 (Metraipolis archive dc_2e8039394251, sha256 a4c5becc87187c7e2455085c4304c586d0b001fbccd30968bc6e78c9e72b5cca)

BibTeX

@misc{dc2e8039394251,
  title = {SP-Guide: Enhancing Aerial Vision-Language Navigation with Spatial Priors — arXiv:2610.32697},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.32697},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_2e8039394251; sha256 a4c5becc87187c7e2455085c4304c586d0b001fbccd30968bc6e78c9e72b5cca},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.32697. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Aerial vision-language navigation (AVLN) requires a UAV to reach a language-described target from partial egocentric observations while reasoning over large outdoor scenes. Existing approaches typically fuse view-centered RGB observations and globally anchored history maps as generic visual embeddings, leaving cross-modal spatial alignment to be learned from sparse navigation supervision. We present SP-Guide, a transformer- based goal-prediction framework that addresses this weakness of alignment through a Spatial Prior Cross-Modal Encoder, an Instruction Enhanced Decoder, and a Goal Convergence Filter for terminal target refinement. Experiments implemented on the CityNav benchmark show that SP-Guide improves success rate over the representative baselines, with the largest gains on unseen environments. These results indicate that physically grounded spatial priors provide an effective inductive bias for robust AVLN.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2610.32697 — 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 2026-10-26T19:45:33.427Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).