Slam and Beacon Data for Automation of Indoor Construction Progress Tracking
Bibliographic record
Abstract
Abstract Construction progress tracking and monitoring is a complex process that is crucial for the successful execution of projects and the delivery of a high-quality product to the client. However, these tasks remain mostly manual - time-consuming and error prone, which often leads to suboptimal quality, cost and schedule overruns. The present research proposes the use of an autonomous rover for the data collection from construction site. The objective is to create a hybrid data processing system using point clouds from the Simultaneous Localization and Mapping (SLAM) algorithm used for robot navigation, and beacons’ data integrated with BIM, to track construction progress and provide project managers with reliable information in quasireal time. The paper is composed of four parts: first, a literature review of best practices regarding the technology used for progress tracking is performed. Second, we propose a framework for automated data collection and information processing for automated progress tracking and monitoring. Third, we present a real-world case study partially implementing the framework by using data acquired by an autonomous rover and BIM, and simulate a real-time reconstruction of the construction site status. Finally, the results are discussed, and future work is identified.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".