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Odometer-Agnostic Drift Correction Using OpenStreetMap Lane Geometry — arXiv:2609.10336

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.10336
License (as archived): CC0-1.0
Archived: 2026-10-16T11:12:04.760Z
SHA-256 of the archived text: d0e85b6c1552a11de436cd523916968538d1182f16db89f36e1ccad2247261de
Stable URL of this page: https://metraipolis.com/documento/dc_faeeb175133a

How to cite

Odometer-Agnostic Drift Correction Using OpenStreetMap Lane Geometry — arXiv:2609.10336 — arXiv (en), https://arxiv.org/abs/2609.10336 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_faeeb175133a (Metraipolis archive dc_faeeb175133a, sha256 d0e85b6c1552a11de436cd523916968538d1182f16db89f36e1ccad2247261de)

BibTeX

@misc{dcfaeeb175133a,
  title = {Odometer-Agnostic Drift Correction Using OpenStreetMap Lane Geometry — arXiv:2609.10336},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.10336},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_faeeb175133a; sha256 d0e85b6c1552a11de436cd523916968538d1182f16db89f36e1ccad2247261de},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.10336. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Despite significant progress in odometry estimation, long-term drift remains a fundamental limitation of incremental pose integration, especially in large-scale or loop-free environments. Existing map-assisted methods can reduce drift, but often depend on dense maps, sensor-specific processing, or complex matching pipelines. We propose a lightweight open-source, odometry-agnostic correction method that aligns short trajectory segments to OpenStreetMap (OSM) lane centerlines. By formulating drift correction as a direct alignment between recent odometry and sparse lane geometry, the method enables efficient online operation without dense priors or expensive preprocessing. Experiments with LiDAR and visual odometry backends demonstrate consistent improvements, with particularly strong gains under severe drift.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-10 in cs.RO).
Categorie arXiv: Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2609.10336 — 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 2026-10-16T10:19:11.932Z dalla pagina https://arxiv.org/catchup/cs.RO/2026-09-10?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).