# 分布式ISAC赋能的缺失二维LiDAR测量多模态恢复

> 原文链接：https://robotics.hellobike.com/papers/2026-09-19-2609-22742
> 频道：papers | 更新日期：2026-09-19 | 标签：arxiv, cs.RO
> 核心摘要：针对二维LiDAR扫描缺失区域恢复，提出分布式ISAC框架融合60-GHz波束训练与LiDAR，并在室内测量中验证。

# 分布式ISAC赋能的缺失二维LiDAR测量多模态恢复

::: v-pre
> 原文链接：[Distributed ISAC-Enabled Multimodal Recovery of Missing 2-D LiDAR Measurements](https://arxiv.org/abs/2609.22742)（arXiv 2609.22742 · 发布日期：2026-09-19）

**Distributed ISAC-Enabled Multimodal Recovery of Missing 2-D LiDAR Measurements**

针对二维LiDAR扫描缺失区域恢复，提出分布式ISAC框架融合60-GHz波束训练与LiDAR，并在室内测量中验证。

## 核心要点

- LiDAR单独重建依赖缺失区周边测量，扇区增大或几何复杂时会退化。
- 方法利用多接收器60-GHz定向波束训练，构建RF表面先验并与LiDAR融合。
- 室内实验含55个接收视点、37,620个波束对功率测量和72个缺失案例。
- 80度缺失扇区中，RF+polar融合将MAE由2.271 m降至0.617 m；少量波束对下仍可恢复。

**验证范围与局限**：仅在室内环境、55个接收视点和72个受控缺失扇区案例上评估。

## 英文摘要 (Abstract)

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.

作者：Mohammed E. Eltayeb
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## 原始参考来源 (Sources)

- [arXiv 2609.22742：Distributed ISAC-Enabled Multimodal Recovery of Missing 2-D LiDAR Measurements](https://arxiv.org/abs/2609.22742)