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Improve dataset card: Add robotics task category, abstract, and enhance formatting (#1)

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- Improve dataset card: Add robotics task category, abstract, and enhance formatting (d051f0175177cb5353207960989f4b626ae6d746)


Co-authored-by: Niels Rogge <[email protected]>

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  1. README.md +12 -4
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- # LoFi
 
 
 
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- The description is generated by Grok3.
 
 
 
 
 
 
 
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  ## Dataset Description
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- - **Repository:** [RS2002/LoFi: Official Repository for The Paper, LoFi: Vision-Aided Label Generator for Wi-Fi Localization and Tracing](https://github.com/RS2002/LoFi)
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- - **Paper:** [LoFi: Vision-Aided Label Generator for Wi-Fi Localization and Tracking](https://arxiv.org/abs/2412.05074), IEEE Globecom GenAI NGN Workshop 2025
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  - **Contact:** [[email protected]](mailto:[email protected])
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  - **Collectors:** Zijian Zhao, Tingwei Chen
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  - **Organization:** AI-RAN Lab (hosted by Prof. Guangxu Zhu) in SRIBD, CUHK(SZ)
 
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+ ---
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+ task_categories:
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+ - robotics
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+ ---
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+ # LoFi: Vision-Aided Label Generator for Wi-Fi Localization and Tracking
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+ Paper: [LoFi: Vision-Aided Label Generator for Wi-Fi Localization and Tracking](https://arxiv.org/abs/2412.05074)
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+ Code: [https://github.com/RS2002/LoFi](https://github.com/RS2002/LoFi)
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+
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+ ## Abstract
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+ 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 this https URL .
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  ## Dataset Description
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  - **Contact:** [[email protected]](mailto:[email protected])
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  - **Collectors:** Zijian Zhao, Tingwei Chen
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  - **Organization:** AI-RAN Lab (hosted by Prof. Guangxu Zhu) in SRIBD, CUHK(SZ)