arXiv:2507.20268v3 Announce Type: replace Abstract: Wireless indoor localization using predictive models with received signal strength information (RSSI) requires proper calibration for reliable position estimates. One remedy is to employ synthetic labels produced by a (generally different) predictive model. But fine-tuning an additional predictor, as well as estimating residual bias of the synthetic labels, demands additional data, aggravating calibration data scarcity in wireless environments. This letter proposes an approach that efficiently uses limited calibration data to simultaneously f
This research addresses ongoing challenges in wireless indoor localization, a critical component for AI and robotics applications, indicating incremental progress.
Improved indoor localization is essential for the effective deployment of autonomous systems and enhanced user experiences in complex environments.
The proposed method offers a more efficient way to calibrate wireless indoor localization systems with limited data, potentially lowering deployment barriers.
- · AI/ML researchers
- · Robotics companies
- · Logistics and industrial automation
More accurate and reliable location data becomes available for various applications.
This could facilitate the development and adoption of advanced indoor navigation and tracking systems.
Improved localization might indirectly contribute to the efficiency of AI agents operating in physical spaces.
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