Metraipolis › archive › dc_899c3faccc06

SyncHeC: Markerless Spatio-Temporal Hand-Eye Calibration via Differentiable Rendering and Continuous-Time Joint Optimization — arXiv:2611.02999

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.02999
License (as archived): CC0-1.0
Archived: 2026-11-03T18:57:23.927Z
SHA-256 of the archived text: fbfe524910979157dfbf65dee2b63d7b5a24d47cdbb963ce6b290c9e4492c9b8
Stable URL of this page: https://metraipolis.com/documento/dc_899c3faccc06

How to cite

SyncHeC: Markerless Spatio-Temporal Hand-Eye Calibration via Differentiable Rendering and Continuous-Time Joint Optimization — arXiv:2611.02999 — arXiv (en), https://arxiv.org/abs/2611.02999 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_899c3faccc06 (Metraipolis archive dc_899c3faccc06, sha256 fbfe524910979157dfbf65dee2b63d7b5a24d47cdbb963ce6b290c9e4492c9b8)

BibTeX

@misc{dc899c3faccc06,
  title = {SyncHeC: Markerless Spatio-Temporal Hand-Eye Calibration via Differentiable Rendering and Continuous-Time Joint Optimization — arXiv:2611.02999},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.02999},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_899c3faccc06; sha256 fbfe524910979157dfbf65dee2b63d7b5a24d47cdbb963ce6b290c9e4492c9b8},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.02999. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Markerless hand-eye calibration under continuous motion requires jointly resolving camera geometry and the time offset between images and joint states. We present SyncHeC (Synchronized Hand-Eye Calibration), a dense differentiable-rendering framework that estimates both from a single robot trajectory. Continuous-time forward kinematics makes latency differentiable, metric depth complements mask contours, and an offline planner combines spatial sensitivity with temporal excitation. Alternating updates support Eye-to-Hand, Eye-in-Hand, and multi-camera calibration. On a 7-DoF arm, one $6.5$\,s motion yields $0.21$\,cm translation error against a marker-board reference, $0.34$\,cm touch-test error, and $2.7$\,ms latency error relative to an $87$\,ms reference with $95\%$ uncertainty of $\pm1.5$\,ms. Against CM-Calib on matched recordings, translation and nominal latency errors improve by $3.7\times$ and $2.3\times$. Experiments span seven arms and four depth sensors. Compensating the recovered offset during policy training and deployment increases success by $8.1$ percentage points across six families and six tasks ($1{,}800$ matched pairs; paired $95\%$ confidence interval $[6.3,9.9]$ points).

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv cs.RO, nuovi lavori del giorno).
Categorie arXiv: Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2611.02999 — 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 2026-11-03T18:17:07.766Z dalla pagina https://arxiv.org/list/cs.RO/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).