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ReVNM: Learning-Based Visual Navigation from a Remote Camera — arXiv:2609.28976

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.28976
License (as archived): CC0-1.0
Archived: 2026-10-08T07:16:37.416Z
SHA-256 of the archived text: dfb9cab42ed1f440383fda859774a5b74bc892374a7da1f7fbd3c7d3735ca3a0
Stable URL of this page: https://metraipolis.com/documento/dc_8a6d39aa61bb

How to cite

ReVNM: Learning-Based Visual Navigation from a Remote Camera — arXiv:2609.28976 — arXiv (en), https://arxiv.org/abs/2609.28976 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_8a6d39aa61bb (Metraipolis archive dc_8a6d39aa61bb, sha256 dfb9cab42ed1f440383fda859774a5b74bc892374a7da1f7fbd3c7d3735ca3a0)

BibTeX

@misc{dc8a6d39aa61bb,
  title = {ReVNM: Learning-Based Visual Navigation from a Remote Camera — arXiv:2609.28976},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.28976},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_8a6d39aa61bb; sha256 dfb9cab42ed1f440383fda859774a5b74bc892374a7da1f7fbd3c7d3735ca3a0},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.28976. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Visual Navigation Models (VNMs) enable robots to navigate from egocentric visual observations without geometric localization and planning, but long-range navigation still requires pre-built maps. This paper presents the Remote Visual Navigation Model (ReVNM), which uses a single remote surveillance camera to serve as both an observation source and an implicit environmental map for visual navigation. While the use of remote cameras could eliminate the need for pre-built maps as well as onboard vision processing, their limited field of view instead of egocentric observations makes it hard to achieve collision-free navigation. The lack of existing data with diverse remote viewpoints, which are crucial for training robust VNMs, further complicates the challenge. In this work, we propose a learning-by-synthesis approach to address this two-fold challenge. Our ReVNM extends a state-of-the-art VNM architecture with an exocentric-to-egocentric (exo2ego) module that predicts an egocentric depth observation from remote-camera observations. This helps the VNM to plan a path while considering obstacles in front of the robot. Trained only on randomly generated worlds with diverse obstacle layouts and camera viewpoints, ReVNM can generalize well to real robot navigation without additional fine-tuning. Experiments in both simulation and real-world environments confirmed the effectiveness of the proposed approach.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-25 in cs.RO).
Categorie arXiv: Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2609.28976 — 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 2026-10-08T06:38:53.764Z dalla pagina https://arxiv.org/catchup/cs.RO/2026-09-25?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).