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RoughSense: Lightweight Terrain-Induced Rover Vibration Prediction Using Point Clouds and IMU Feedback — arXiv:2609.03720

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.03720
License (as archived): CC0-1.0
Archived: 2026-10-17T02:57:32.226Z
SHA-256 of the archived text: 95b43216e83f3c49f50e2b046eba842558e4a41e8d4cba26875e028323623183
Stable URL of this page: https://metraipolis.com/documento/dc_3cfceff41809

How to cite

RoughSense: Lightweight Terrain-Induced Rover Vibration Prediction Using Point Clouds and IMU Feedback — arXiv:2609.03720 — arXiv (en), https://arxiv.org/abs/2609.03720 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_3cfceff41809 (Metraipolis archive dc_3cfceff41809, sha256 95b43216e83f3c49f50e2b046eba842558e4a41e8d4cba26875e028323623183)

BibTeX

@misc{dc3cfceff41809,
  title = {RoughSense: Lightweight Terrain-Induced Rover Vibration Prediction Using Point Clouds and IMU Feedback — arXiv:2609.03720},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.03720},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_3cfceff41809; sha256 95b43216e83f3c49f50e2b046eba842558e4a41e8d4cba26875e028323623183},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.03720. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Autonomous navigation in space requires reliable terrain assessment for safe operations, especially in underground environments with limited communication, computing resources, and power budget. This paper presents a lightweight method for real-time vibration-aware traversability mapping using a Light Detecting And Ranging (LiDAR) point cloud and Inertial Measurement Unit (IMU) measurements. An initial vibration proxy is estimated from terrain geometry by applying Random sample consensus (RANSAC) to local point-cloud patches produced by a Simultaneous Localisation And Mapping (SLAM) algorithm. In parallel, the IMU provides local observations of the vibration experienced by the rover during traversal. The point-cloud-based prediction is then corrected online using Recursive Least Squares, allowing the system to adapt the geometric estimate to the measured rover response. The approach is evaluated in a lunar analogue environment, an outdoor field, and an underground mine.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-04 in cs.RO).
Categorie arXiv: Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2609.03720 — 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 2026-10-17T02:33:25.400Z dalla pagina https://arxiv.org/catchup/cs.RO/2026-09-04?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).