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Visual-Inertial RGB-D SLAM with Encoders for a Differential Wheeled Robot — arXiv:2610.41768

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.41768
License (as archived): CC0-1.0
Archived: 2026-11-03T05:47:29.390Z
SHA-256 of the archived text: 9fc085d6ca4a00ed203e3879e6a211fc38f048591ac031f0616a785e5c6bc10c
Stable URL of this page: https://metraipolis.com/documento/dc_dead220d53fc

How to cite

Visual-Inertial RGB-D SLAM with Encoders for a Differential Wheeled Robot — arXiv:2610.41768 — arXiv (en), https://arxiv.org/abs/2610.41768 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_dead220d53fc (Metraipolis archive dc_dead220d53fc, sha256 9fc085d6ca4a00ed203e3879e6a211fc38f048591ac031f0616a785e5c6bc10c)

BibTeX

@misc{dcdead220d53fc,
  title = {Visual-Inertial RGB-D SLAM with Encoders for a Differential Wheeled Robot — arXiv:2610.41768},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.41768},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_dead220d53fc; sha256 9fc085d6ca4a00ed203e3879e6a211fc38f048591ac031f0616a785e5c6bc10c},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.41768. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Recent years have seen multiple impressive results in visual-inertial odometry (VIO) techniques, by which accurate state estimation can be achieved via the extended Kalman filter (EKF) or nonlinear optimization. However, these approaches are rarely open source, and they tend to fail in real experiments due to the temporary lack of feature points and fast motion. Therefore, in this study, we used encoders to overcome the temporary failure of purely vision-based simultaneous localization and mapping (SLAM). Here, we propose a generative measurement model for encoders and derive an expression for the maximum a posteriori (MAP) estimate and necessary Jacobians for optimization. We use our theory to present a novel tightly coupled visual-inertial encoder RGB-Depth (RGB-D) SLAM system. Tests on our system on an in-house dataset confirmed that our modeling effort led to accurate (with a root mean squared error (RMSE) of approximately 2-7 cm) and robust state estimation in real time. The source code and our dataset containing the encoder information have been published for verification.

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/2610.41768 — 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 2026-11-03T05:33:48.028Z dalla pagina https://arxiv.org/list/cs.RO/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).