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FIRE-LIVWO: Robust LiDAR-Inertial-Visual-Wheel Odometry via Failure-Immune mmWave Radar Enhancement — arXiv:2609.05325

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.05325
License (as archived): CC0-1.0
Archived: 2026-10-16T23:51:41.437Z
SHA-256 of the archived text: fe7393cd8f908261d74cb6f0f3bb6442d7441e0ed772bc391442df16b0ea458d
Stable URL of this page: https://metraipolis.com/documento/dc_7744570acfa3

How to cite

FIRE-LIVWO: Robust LiDAR-Inertial-Visual-Wheel Odometry via Failure-Immune mmWave Radar Enhancement — arXiv:2609.05325 — arXiv (en), https://arxiv.org/abs/2609.05325 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_7744570acfa3 (Metraipolis archive dc_7744570acfa3, sha256 fe7393cd8f908261d74cb6f0f3bb6442d7441e0ed772bc391442df16b0ea458d)

BibTeX

@misc{dc7744570acfa3,
  title = {FIRE-LIVWO: Robust LiDAR-Inertial-Visual-Wheel Odometry via Failure-Immune mmWave Radar Enhancement — arXiv:2609.05325},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.05325},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_7744570acfa3; sha256 fe7393cd8f908261d74cb6f0f3bb6442d7441e0ed772bc391442df16b0ea458d},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.05325. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Achieving robust SLAM in large-scale underground coal mines with complex structures and severe degeneracies remains highly challenging. Dense smoke and dust cause substantial loss of visual information and degrade LiDAR point-cloud features, while long, self-similar corridors induce geometric degeneration, leading to pronounced odometry drift. To address these issues, we propose FIRE-LIVWO: Failure-Immune mmWave Radar-Enhanced LiDAR-Inertial-Visual-Wheel Odometry, a tightly coupled multi-modal odometry framework based on an iterated error-state Kalman filter (IESKF). The framework fuses 4D mmWave radar, LiDAR, and visual features within a unified VoxelMap and jointly constructs LiDAR-radar point-to-plane residuals and sparse visual photometric residuals. In smoke-filled environments, we exploit the strong penetration of 4D mmWave radar and introduce pointwise Doppler velocity constraints to preserve state observability. In geometrically degenerate corridors, we tightly couple wheel odometry using non-holonomic constraints (NHC) and online lever-arm compensation to reduce drift. Our central contribution is a degeneration detection and adaptive fusion model switching strategy grounded in geometric and visual observability analysis, which quantifies observability online and dynamically adjusts modality weights. Real-world experiments in underground coal mines demonstrate that FIRE-LIVWO accurately identifies failure boundaries, enabling reliable modality switching under extreme conditions. Compared with baselines, it achieves superior accuracy and robustness (average localization error of 5.677m). We open source our code on Github to benefit the robotics community.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-07 in cs.RO).
Categorie arXiv: Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2609.05325 — 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 2026-10-16T23:21:38.656Z dalla pagina https://arxiv.org/catchup/cs.RO/2026-09-07?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).