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From Optical Flow to Safety: Visual Safety Certification via Scale-Free Relative Motion — arXiv:2610.21374

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.21374
License (as archived): CC0-1.0
Archived: 2026-10-16T10:39:54.293Z
SHA-256 of the archived text: 9281b003b8959c3e6847289e615db21d3102b01a694e52a74bbef951dbf8e752
Stable URL of this page: https://metraipolis.com/documento/dc_a6fb35fc00ce

How to cite

From Optical Flow to Safety: Visual Safety Certification via Scale-Free Relative Motion — arXiv:2610.21374 — arXiv (en), https://arxiv.org/abs/2610.21374 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_a6fb35fc00ce (Metraipolis archive dc_a6fb35fc00ce, sha256 9281b003b8959c3e6847289e615db21d3102b01a694e52a74bbef951dbf8e752)

BibTeX

@misc{dca6fb35fc00ce,
  title = {From Optical Flow to Safety: Visual Safety Certification via Scale-Free Relative Motion — arXiv:2610.21374},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.21374},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_a6fb35fc00ce; sha256 9281b003b8959c3e6847289e615db21d3102b01a694e52a74bbef951dbf8e752},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.21374. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Safe autonomous navigation using monocular vision remains challenging because explicit obstacle states are not directly observable. This letter introduces a visual safety-certification paradigm that uses scale-free, depth-normalized relative motion encoded by monocular optical flow as the representation for collision-risk certification, without reconstructing obstacle position, velocity, geometry, or a metric world model. Unlike conventional optical-flow-based navigation approaches that rely on image motion to shape avoidance behavior, the proposed framework directly encodes collision-cone geometry and safety constraints in this relative-motion representation. An online Concurrent Learning observer estimates the normalized relative velocity and provides a computable estimation-error bound. This uncertainty is propagated into the collision cone through angular inflation and a monocular robot-footprint margin, yielding a Robust Vision-Based Collision Cone Control Barrier Function (RV-C$^3$BF). An optimal-decay CBF quadratic program minimally modifies the nominal control input to enforce the resulting robust safety constraint under the stated assumptions. Extensive simulation and real-world experiments on a TurtleBot3 in cluttered environments with static and dynamic obstacles demonstrate collision avoidance using only monocular vision and onboard proprioception, with the estimation-error bound containing the measured error in $98.3\%$ of simulation samples and the robot achieving collision-free navigation in $93$ of $100$ real-world trials.

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv cs.RO, nuovi lavori del giorno).
Categorie arXiv: Robotics (cs.RO) ; Systems and Control (eess.SY).
Pagina del lavoro: https://arxiv.org/abs/2610.21374 — 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 2026-10-16T08:52:17.636Z dalla pagina https://arxiv.org/list/cs.RO/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).