Indoor Navigation with iPhone/iPad: Floor Plan Based Monocular Vision Navigation
Bibliographic record
Abstract
Pedestrian device like smart phone and tablet nowadays become more and more intelligent and the interest to apply those smart devices for indoor navigation is growing. Most of such smart devices are integrated with a GPS chip, an inertial sensor(s), a magnetic compass along with other gadgets such as camera and Wi-Fi. The large varieties of sensors enable hybrid location solution to not only improve availability of indoor positioning but also the accuracy and smoothness. But in deep indoor scenario, the positioning accuracy is seldom satisfactory and suffers from accumulative error due to dead-reckoning sensors. In this paper, a floor plan based vision navigation method is designed for pedestrian handset indoor application. The floor plan for buildings is an easily accessible online resource. Besides of the fact that floor plan is a useful indoor map containing detailed paths and rooms, vision measurement will be matched with floor plan to derive accurate and drift-free position. The Random Sample Consensus (RANSAC) algorithm is adopted for robust matching between the floor plan and the camera image. An iPhone demo App is developed and tested on real device with indoor tests conducted in the University of Calgary. The derived iPhone positions are compared with the landmark reference and the results indicate meter-level horizontal accuracy. With such accuracy, the demo App is also featured with augmented navigation reality which has shown great feasibility and innovation for pedestrian indoor navigation.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".