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Chain-SLAM: Globally Consistent Backend for Multi-Session LiDAR SLAM via Chained Loop Closure — arXiv:2609.12221

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.12221
License (as archived): CC0-1.0
Archived: 2026-10-16T03:58:06.489Z
SHA-256 of the archived text: a79ed8b7c6feb627c6f4bf158a29745e4f167d427b51d32739695e2855cb60a9
Stable URL of this page: https://metraipolis.com/documento/dc_dbfa3665640a

How to cite

Chain-SLAM: Globally Consistent Backend for Multi-Session LiDAR SLAM via Chained Loop Closure — arXiv:2609.12221 — arXiv (en), https://arxiv.org/abs/2609.12221 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_dbfa3665640a (Metraipolis archive dc_dbfa3665640a, sha256 a79ed8b7c6feb627c6f4bf158a29745e4f167d427b51d32739695e2855cb60a9)

BibTeX

@misc{dcdbfa3665640a,
  title = {Chain-SLAM: Globally Consistent Backend for Multi-Session LiDAR SLAM via Chained Loop Closure — arXiv:2609.12221},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.12221},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_dbfa3665640a; sha256 a79ed8b7c6feb627c6f4bf158a29745e4f167d427b51d32739695e2855cb60a9},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.12221. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Maintaining consistency over long spatial and temporal horizons remains a fundamental challenge in large-scale LiDAR SLAM, particularly when integrating maps collected across multiple sessions. We present Chain-SLAM, a LiDAR SLAM backend enabling online multi-session map alignment and reuse with global consistency at large scale. We implement a chained loop closure mechanism that efficiently propagates geometric constraints across inter-session keyframes through an adjacency graph, enabling robust long-horizon consistency triggered by reliable short-horizon loop closures. The system initializes inter-session alignment with GNSS-proximity place recognition, then performs on-the-fly loop closure detections and joint optimization of loaded maps and newly acquired trajectories within a unified factor graph, maintaining both inter- and intra-session geometric consistency without dynamic object removal, and cross-platform robustness with minimal hyperparameter tuning. Experimental results show improved trajectory accuracy and robust multi-session integration on large-scale datasets. We release our source code to support reproducible research in large-scale multi-session LiDAR SLAM. Project site: this https URL

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-14 in cs.RO).
Categorie arXiv: Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2609.12221 — 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 2026-10-16T03:44:44.702Z dalla pagina https://arxiv.org/catchup/cs.RO/2026-09-14?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).