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Dr-LiSA: Direct Radar-Lidar Scan Alignment for SE(3) Localization — arXiv:2609.26423

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.26423
License (as archived): CC0-1.0
Archived: 2026-10-08T16:43:56.850Z
SHA-256 of the archived text: 197194b39d1f3b9ee2453e292abd9f2d54b45842b23184d2b28aae0dba6ef999
Stable URL of this page: https://metraipolis.com/documento/dc_10618bc4bd4e

How to cite

Dr-LiSA: Direct Radar-Lidar Scan Alignment for SE(3) Localization — arXiv:2609.26423 — arXiv (en), https://arxiv.org/abs/2609.26423 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_10618bc4bd4e (Metraipolis archive dc_10618bc4bd4e, sha256 197194b39d1f3b9ee2453e292abd9f2d54b45842b23184d2b28aae0dba6ef999)

BibTeX

@misc{dc10618bc4bd4e,
  title = {Dr-LiSA: Direct Radar-Lidar Scan Alignment for SE(3) Localization — arXiv:2609.26423},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.26423},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_10618bc4bd4e; sha256 197194b39d1f3b9ee2453e292abd9f2d54b45842b23184d2b28aae0dba6ef999},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.26423. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

This paper introduces Dr-LiSA, a first-of-its-kind direct method for localizing 2D spinning radar intensity measurements in $SE(3)$ against 3D lidar maps. Radar-lidar localization combines the complementary strengths of the two sensing modalities: radar is robust to adverse weather and precipitation, while lidar provides high-fidelity 3D maps in favourable conditions. However, existing radar-lidar localization methods are restricted to planar $SE(2)$ localization and have generally fallen short of the accuracy achieved by lidar-lidar and even radar-radar systems. A key challenge is the substantial sensing-modality gap between radar and lidar, which observe and represent scene structure in fundamentally different ways. Dr-LiSA bridges this gap using a learned forward model that predicts radar measurements from a lidar submap at a candidate pose, enabling direct photometric alignment of predicted and observed radar scans in $SE(3)$. Dr-LiSA outperforms prior radar-lidar approaches in $SE(2)$ while achieving planar accuracy competitive with state-of-the-art radar-radar localization across more than 90 km of on-road data.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-23 in cs.RO).
Categorie arXiv: Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2609.26423 — 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 2026-10-08T15:56:53.499Z dalla pagina https://arxiv.org/catchup/cs.RO/2026-09-23?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).