System for Drone-Based Indoor Mapping for Augmented Reality
Bibliographic record
Abstract
Indoor Simultaneous Localization and Mapping (SLAM) is essential for autonomous navigation in various applications such as architectural modeling, facility security, or factory quality control[1], but existing solutions often require significant costs including knowledge, and specialized equipment. Utilizing consumer drones (e.g. DJI Mini 3) offers a cost-effective and accessible alternative but challenges exist due to limitations in processing power and sensor capabilities. This paper introduces a real-time indoor SLAM system using a consumer drone and the provided on-board sensor with simple setup procedures building on previous simulation only studies. The proposed system operates without pre-programmed paths and does not rely on external positioning systems such as Ultra-Wideband (UWB) technology. Experimental evaluations demonstrate the system's effectiveness in mapping indoor environments, highlighting considerable improvements over traditional methods that require more complex equipment. The versatility and reliability of the proposed approach provides a foundation for actual use making it a promising tool for broader applications, lowering the barriers for accessible autonomous navigation solutions.
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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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.012 |
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".