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HydroMap: Probabilistic Water Surface Elevation Mapping for Semantic Scene Representation in Inland Waterways — arXiv:2609.14903

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.14903
License (as archived): CC0-1.0
Archived: 2026-10-16T02:14:41.440Z
SHA-256 of the archived text: ef9219fe773c0b04eb7cbe0e7cd30c99bf8c5bbcba544ae5b0ee890e6efbc84a
Stable URL of this page: https://metraipolis.com/documento/dc_dcb45708d966

How to cite

HydroMap: Probabilistic Water Surface Elevation Mapping for Semantic Scene Representation in Inland Waterways — arXiv:2609.14903 — arXiv (en), https://arxiv.org/abs/2609.14903 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_dcb45708d966 (Metraipolis archive dc_dcb45708d966, sha256 ef9219fe773c0b04eb7cbe0e7cd30c99bf8c5bbcba544ae5b0ee890e6efbc84a)

BibTeX

@misc{dcdcb45708d966,
  title = {HydroMap: Probabilistic Water Surface Elevation Mapping for Semantic Scene Representation in Inland Waterways — arXiv:2609.14903},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.14903},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_dcb45708d966; sha256 ef9219fe773c0b04eb7cbe0e7cd30c99bf8c5bbcba544ae5b0ee890e6efbc84a},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.14903. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Autonomous surface vehicles operating in inland waterways require a persistent representation of both surrounding structures and the water surface. LiDAR-based simultaneous localization and mapping often produces sparse or missing water returns, leaving this operational surface absent from the reconstructed scene. We propose HydroMap, an odometry-decoupled framework that reconstructs water surface elevation from stereo observations and integrates it with the structural map. Per-frame water points form joint cell observations with propagated stereo and pose uncertainty, and successive observations are fused into a persistent probabilistic elevation map. Semantic map conversion then combines the elevation map with structural geometry in a unified 2.5D representation of water, boundaries, structures, and overhead regions. On the Pohang Canal and Leuven Vaart datasets, the elevation RMSE remains below 5 cm relative to LiDAR references expressed in the same map frame. The elevation and semantic maps are published at 2 Hz and 1 Hz, respectively. HydroMap thereby complements LiDAR maps with a persistent representation of the water surface for downstream navigation in inland waterways.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-15 in cs.RO).
Categorie arXiv: Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2609.14903 — 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 2026-10-16T01:52:32.346Z dalla pagina https://arxiv.org/catchup/cs.RO/2026-09-15?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).