外观
论文arXiv 2610.1273320 天前
面向信号发射设备的视觉-Wi-Fi导航
原文链接：Visual-Wi-Fi Navigation to Signal-Emitting Devices（arXiv 2610.12733 · 发布日期：2026-10-08）
Visual-Wi-Fi Navigation to Signal-Emitting Devices
研究机器人在未建图室内利用视觉与旁听Wi-Fi包寻找发射设备，提出WiFiSpaces与ViFiNav并在仿真和真机验证。
核心要点
- 小型、遮挡或视觉不显著目标难以室内导航，Wi-Fi信号可提供空间线索。
- WiFiSpaces在Habitat中加入在线光线追踪Wi-Fi感知，支持连续位姿闭环学习。
- ViFiNav用运动聚合多视角方位并融合深度，随信源估计更新学习决策。
- 实验显示利用机器人运动提升定位，WiFiSpaces微调提高成功率，并可零样本迁移和真机部署。
验证范围与局限：验证包括WiFiSpaces仿真、未见场景数据集零样本迁移和一台真实机器人，且未进行真实世界训练。
英文摘要 (Abstract)
Indoor navigation to task-relevant targets remains difficult when the target is small, occluded, or visually indistinct. Meanwhile, smart appliances, mobile devices, and IoT tags emit Wi-Fi signals during everyday communication, providing a pervasive but underused spatial cue. We study visual-Wi-Fi navigation, where a robot reaches a signal-emitting device in an unmapped environment using onboard visual observations and Wi-Fi packets overheard from the device, without connecting to the device or modifying infrastructure. Realizing this requires pose-dependent Wi-Fi observations during closed-loop policy learning and reliable spatial cues from bearing measurements corrupted by indoor multipath propagation on a moving robot. We present WiFiSpaces, which extends the Habitat embodied simulator with online ray-traced Wi-Fi sensing, rendering Wi-Fi observations for a robot-mounted antenna array at continuous poses during policy rollouts. On top of it, we build ViFiNav, a localization-aware navigation framework: an online source-localization algorithm exploits robot motion to disambiguate and robustly aggregate bearings from multiple viewpoints into a confidence-aware source estimate, and a learned policy fuses this evolving estimate with depth observations and learns to act on it as it is refined. Experiments show that exploiting robot motion is key to accurate source estimates, and that fine-tuning on ray-traced Wi-Fi observations in WiFiSpaces improves the success rate of ViFiNav. The policy transfers zero-shot to unseen scene datasets, and we deploy it on a real robot with a Wi-Fi sensor built from a commodity router, without any real-world training.
作者：Lihao Wang, Tianjian Xu, Tianmin Shu, Renjie Zhao
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