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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".