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Benchmarking Visual-Inertial Odometry in Subterranean Environments Under Sensor Degradation, Miscalibration, and Dynamic Occlusion — arXiv:2609.18628

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.18628
License (as archived): CC0-1.0
Archived: 2026-10-15T16:58:23.162Z
SHA-256 of the archived text: c7e303d1507e69230678ad06b45baa2d20bf09494f659702f91f4570b002b6c3
Stable URL of this page: https://metraipolis.com/documento/dc_2df660ebe92f

How to cite

Benchmarking Visual-Inertial Odometry in Subterranean Environments Under Sensor Degradation, Miscalibration, and Dynamic Occlusion — arXiv:2609.18628 — arXiv (en), https://arxiv.org/abs/2609.18628 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_2df660ebe92f (Metraipolis archive dc_2df660ebe92f, sha256 c7e303d1507e69230678ad06b45baa2d20bf09494f659702f91f4570b002b6c3)

BibTeX

@misc{dc2df660ebe92f,
  title = {Benchmarking Visual-Inertial Odometry in Subterranean Environments Under Sensor Degradation, Miscalibration, and Dynamic Occlusion — arXiv:2609.18628},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.18628},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_2df660ebe92f; sha256 c7e303d1507e69230678ad06b45baa2d20bf09494f659702f91f4570b002b6c3},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.18628. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Visual-inertial odometry (VIO) is a core capability for autonomous operation in GPS-denied subterranean environments, yet its reliability can degrade sharply under sensor drift, calibration errors, and dynamic occlusion. Existing evaluations mainly emphasize nominal-condition accuracy, offering limited insight into when practical deployment failures occur. In this work, we present a failure-centric stress-test benchmark for VIO in underground environments using the CERBERUS dataset. We systematically evaluate four representative VIO systems spanning filtering-, optimization-, and learning-based paradigms under nine practical perturbation settings, including IMU bias and noise variation, camera intrinsic and extrinsic drift, and dynamic scene occlusion. Beyond conventional trajectory error, we analyze robustness limits through coverage ratio and failure thresholds, revealing breakdown behaviors that are not captured by nominal-condition performance alone. Our study shows distinct vulnerability patterns across VIO paradigms: some methods are more sensitive to inertial degradation, while others are more affected by geometric miscalibration or dynamic interference. These results provide deployment-oriented guidance for VIO selection, calibration prioritization, and reliable operation in challenging underground scenarios. To support reproducible evaluation and future extensions, we will release the full benchmark scripts and evaluation pipeline.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-17 in cs.RO).
Categorie arXiv: Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2609.18628 — 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 2026-10-15T16:13:27.308Z dalla pagina https://arxiv.org/catchup/cs.RO/2026-09-17?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).