High Accuracy, 6-DoF Simultaneous Localization and Calibration using Visible Light Positioning
Bibliographic record
Abstract
Benefiting from the development of image sensors and the popularity of light-emitting diode (LED) lighting technology, visible light positioning (VLP) technology based on image sensors has ushered in vigorous development and broad prospects, which can provide low-cost and high-accuracy position service. However, the existing approaches require dense LEDs or sensors such as gyroscopes to assist positioning, which limit the area and lower the accuracy of positioning because of the errors from imperfect sensors. In this paper, we propose a simultaneous localization and calibration VLP method based on double coplanar circular LED lights aiming to get rid of the dependence on additional sensors and dense LED transmitters. By the pinhole camera model and the perspective projection of circle, our proposed method extends the available position area and relaxes the required quantity of LED to two. The experiment result shows that our system has a mean 3D positioning accuracy of 7.91cm, a mean angle error of less than 1.6°, and an average latency of 182ms on mobile devices.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".