High-Speed Indoor Navigation System based on Visible Light and Mobile Phone
Bibliographic record
Abstract
Visible light positioning (VLP) is widely believed to be a cost-effective answer to the growing demand for real-time indoor positioning. However, due to the high computational cost of image processing, most existing VLC-based systems fail to deliver satisfactory performance in terms of positioning speed and accuracy, both of which are crucial for the performance of indoor navigation. This paper proposes a novel VLP solution that provides accurate and high-speed indoor navigation via the designs of an elaborate flicker-free line coding scheme and a lightweight image processing algorithm. In addition, this solution has the advantage of supporting flicker mitigation and dimming, which are important for illumination. An Android-based system prototype has been developed for field tests on an off-the-shelf smartphone. Experimental results show that it supports indoor positioning for users moving at a speed of up to 18 km/h. In addition, it can achieve a high accuracy of 7.5 cm, and the computational time is reduced to 22.7 ms for single-luminaire and to 35.7 ms for dual-luminaries, respectively.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".