Bibliographic record
Abstract
GPS-based technology has served the positioning and navigation industry for decades with outstanding reliability and accuracy. However, limitations for location-based services in indoor scenarios remain where GPS signal reception suffers from severe degradation or even outages. Wi-Fi-based positioning is currently the most popular indoor location solution, with an availability and time-to-first-fix that are significantly better than GPS. However, the achievable position accuracy is only at the level of tens of meters on average depending on database density and signal reception quality. In order to improve positioning accuracy and availability, motion sensors like accelerometers, gyros, and magnetic compasses are increasingly integrated into smart devices. However, their position solutions suffer from the effects of significant accumulative errors. In this paper, a vision-based indoor positioning method is developed to overcome the limitations above. The proposed vision-based system relies on a single camera, widely available on smart phones and tablets. The derivation of the absolute 3D position from 2D snapshots of a single camera requires the use of an external geo-reference database. In this research, a ubiquitous floor plan database has been used to provide accurate geodetic information. Unlike other popular geo-reference databases, the database used in this work can easily be generated with existing resources. The proposed system has been developed as an iOS App and was tested on iPad for various indoor scenarios. The results show that the performance of the proposed system is superior to Wi-Fi-based positioning systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.004 | 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".