用2D图像生成高精度定位标签，低成本提升Wi-Fi追踪研究
LoFi: Vision-Aided Label Generator for Wi-Fi Localization and Tracking
- 通过摄像头图像自动推算位置坐标，无需昂贵设备
- 在真实场景中实现厘米级定位精度，支持持续追踪
- 适合缺乏专业硬件的科研与开发人员使用
基于数据的Wi-Fi定位与追踪技术因对专用硬件依赖较低而展现出巨大潜力。然而，现有大多数数据采集方法仅提供粗粒度真值或有限数量的标注点，严重制约了数据驱动方法的发展。尽管激光雷达等系统可提供精确真值，但其高昂成本使多数用户难以负担。为此，我们提出LoFi——一种视觉辅助的标签生成器，可仅通过2D图像生成高精度的位置真值坐标，具备高精度、低成本和易用性优势。利用该方法，我们基于ESP32-S3和网络摄像头构建了一个真实的Wi-Fi跟踪与定位数据集。论文代码与数据集已开源：https://github.com/RS2002/LoFi。
原文摘要 · Abstract (English)
Data-driven Wi-Fi localization and tracking have shown great promise due to their lower reliance on specialized hardware compared to model-based methods. However, most existing data collection techniques provide only coarse-grained ground truth or a limited number of labeled points, significantly hindering the advancement of data-driven approaches. While systems like lidar can deliver precise ground truth, their high costs make them inaccessible to many users. To address these challenges, we propose LoFi, a vision-aided label generator for Wi-Fi localization and tracking. LoFi can generate ground truth position coordinates solely from 2D images, offering high precision, low cost, and ease of use. Utilizing our method, we have compiled a Wi-Fi tracking and localization dataset using the ESP32-S3 and a webcam. The code and dataset of this paper are available at https://github.com/RS2002/LoFi.
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