AUGMENTED REALITY BASED INDOOR NAVIGATION USING POINT CLOUD LOCALIZATION
Bibliographic record
Abstract
People of various ages may find it difficult to navigate complex building structures as they become more prevalent. The future belongs to a world that is artificially facilitated, and Augmented Reality will play a significant role in that future. The concept of Indoor Navigation using a smartphone-based Augmented Reality technology is explored in this research. Using readily available and affordable tools, this study proposes a solution to this issue. We built an Augmented Reality-based framework to assist users in navigating a building using ARWAY, a software development toolkit. To find the shortest paths, we used the Point Cloud Localization and A* pathfinding algorithms. A shop inside a shopping Centre, a particular room in a hotel, and other locations can be easily located using this app, and the user is given fairly precise visual assistance through their smartphone to get to his desired spot. The proposed framework is based on augmented reality, and point clouds are the most important components. The application allows the user to choose their desired destination as well as change their destination at any time. To find the results from the technical, subjective, and demographic responses, we used hypothesis testing and validation with statistical analysis and exploratory data analysis methods.
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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) | 0.003 | 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".