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.
@article{arxiv.2412.05074,
title = {LoFi: Vision-Aided Label Generator for Wi-Fi Localization and Tracking},
author = {Zijian Zhao and Tingwei Chen and Fanyi Meng and Zhijie Cai and Hang Li and Xiaoyang Li and Guangxu Zhu},
journal= {arXiv preprint arXiv:2412.05074},
year = {2025}
}