Improving autonomous robotic navigation using IFC files
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it