Tag-Based Indoor Localization of UAVs in Construction Environments: Opportunities and Challenges in Practice
Bibliographic record
Abstract
Automated visual inspection and progress monitoring of construction projects using different robotic platforms have recently attracted scholars’ attention. Unmanned/unoccupied aerial vehicles (UAVs), however, are more and more being used for this purpose because of their maneuverability and perspective capabilities. Although a multi-sensor autonomous UAV can enhance the collection of informative data in constantly-evolving construction environments, autonomous flight and navigation of UAVs are challenging in indoor environments where the global positioning system (GPS) might be denied or unreliable. In such continually changing environments, the limited external infrastructure and the existence of unknown obstacles are two key challenges that need to be addressed. On the other hand, construction indoor environments are not fully unknown, as a progressively updating building information model (BIM) provides valuable prior knowledge about the GPS-denied environment. This fact can potentially create unique opportunities to facilitate the indoor navigation process in construction projects. The authors have previously shown the potentials of AprilTag fiducial markers for localization of a camera-equipped UAV in various controlled experimental setups in the laboratory. In this paper, we investigate the opportunities and challenges of using tag-based localization techniques in real-world construction environments.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".