Improving autonomous robotic navigation using IFC files
Bibliographic record
Abstract
Abstract The navigation of robotic systems in construction sites often relies on sensor data from the robot. While mapping and navigation protocols such as simultaneous localization and mapping (SLAM) are quite useful for navigation, they often require a preliminary mapping of the site, which is usually done manually. Waypoint generation for certain tasks, such as 3D scanning, cannot be done before obtaining said preliminary map, which can be tedious. Building information model (BIM) files contain rich semantic information about buildings; therefore, it is worth considering an approach where the information in BIM is leveraged to minimize the need for manual preliminary mapping of sites. This study proposes a methodology to get information from BIM—in the form of IFC files—to an autonomous robotic system (ARS) in the form of navigation maps, simulation environments, JSON files with useful semantic information, and proposed waypoints for stop-and-go missions. The schedule element present in IFC is used to generate obstacle maps relevant to the level of construction progress at the time the ARS is deployed. The results are validated with a case study of the entire process from the IFC file input to the waypoint generation for an ARS to complete a 3D reconstruction of an indoor space.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.006 | 0.003 |
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".