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Distributed ISAC-Enabled Multimodal Recovery of Missing 2-D LiDAR Measurements — arXiv:2609.22742

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.22742
License (as archived): CC0-1.0
Archived: 2026-10-10T00:55:16.441Z
SHA-256 of the archived text: 85ab4253f892b5999906d69492f151534b84a92d995a61d31417acf05a1c6dcb
Stable URL of this page: https://metraipolis.com/documento/dc_7fe45f16cc92

How to cite

Distributed ISAC-Enabled Multimodal Recovery of Missing 2-D LiDAR Measurements — arXiv:2609.22742 — arXiv (en), https://arxiv.org/abs/2609.22742 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_7fe45f16cc92 (Metraipolis archive dc_7fe45f16cc92, sha256 85ab4253f892b5999906d69492f151534b84a92d995a61d31417acf05a1c6dcb)

BibTeX

@misc{dc7fe45f16cc92,
  title = {Distributed ISAC-Enabled Multimodal Recovery of Missing 2-D LiDAR Measurements — arXiv:2609.22742},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.22742},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_7fe45f16cc92; sha256 85ab4253f892b5999906d69492f151534b84a92d995a61d31417acf05a1c6dcb},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.22742. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Recovering missing regions in 2-D LiDAR scans is crucial for maintaining geometric awareness in environmental sensing. LiDAR-only reconstruction relies on measurements surrounding the missing region and can degrade as the unobserved sector grows or the surface geometry becomes more complex. This paper presents a distributed integrated sensing and communication (ISAC) framework that uses directional 60-GHz beam-training measurements from multiple receivers to assist in the recovery of missing regions in 2-D LiDAR scans. Transmitter and receiver positions, beam directions, and relative beam-power thresholding are used to construct an RF-derived surface prior without requiring RF time-of-flight measurements. The resulting RF-derived range estimates are then fused with LiDAR measurements to estimate the missing geometry. The proposed framework is evaluated in an indoor environment using LiDAR and 60-GHz phased-array measurements from 55 receiver viewpoints comprising 37,620 beam-pair power measurements across 72 controlled missing-sector cases. The results show that RF-assisted reconstruction provides larger accuracy gains as the missing-sector width increases. For the 80-degree missing-sector case investigated in this paper, RF+polar fusion is shown to reduce the mean absolute error (MAE) from 2.271 m for polar LiDAR interpolation to 0.617 m, corresponding to a 72.8% reduction. The results further show that useful RF-assisted recovery can be maintained even with fewer beam-pair measurements under limited beam training. These findings demonstrate that directional communication measurements can support environmental sensing by providing complementary geometric information for missing LiDAR regions.

Tema degli esploratori: calcolo e infrastrutture (retro-catalogo arXiv: annunci del 2026-09-22 in eess.SY).
Categorie arXiv: Systems and Control (eess.SY).
Pagina del lavoro: https://arxiv.org/abs/2609.22742 — 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 2026-10-09T23:57:37.592Z dalla pagina https://arxiv.org/catchup/eess.SY/2026-09-22?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).