Augmented Reality indoor tracking using Placenote
Bibliographic record
Abstract
Abstract Recently, augmented reality technology became more stable and integrated into our daytime applications. Augmented reality adds virtual content to enrich physical environments. Augmented reality uses tracking techniques to capture environments features. The tracking is classified into two types: outdoor and indoor tracking. Currently, outdoor tracking becomes popular for outdoor navigation applications using GPS. However, GPS has low performance in indoor tracking due to the imprecision of GPS satellite signals. The deficiencies in the signals make it difficult to navigate through malls, hospitals, museums, and airports. Indoor tracking provides a solution for vast, complex indoor environments navigation. Nowadays, most indoor applications rely on predefined two- and three-dimensional maps of buildings to direct users to their destinations. Our paper presents an indoor tracking model that combines placenote technology with cloud computing technology and A* navigation algorithm. Our model enables users to select a destination without predefined maps, and at the same time, it can calculate the shortest path. Experiments demonstrate that our suggested model achieves an average accuracy about 99 percent within a 7–10 cm error bound in scenarios involving different distance paths. At the same time, the experiments show that all users reach their destinations successfully. The error of the proposed model is significantly lower than the errors reported in the literature for research conducted with markerless technology and tested in similar area sizes.
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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 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".