Mapping Utility Infrastructure via Underground GPS Positioning with Autonomous Telerobotics
Bibliographic record
Abstract
This paper presents technology applications from the autonomous mining and construction industries in tunnel and underground environments as applied to critical large diameter utility infrastructure. A self-contained inertial navigation system for the positioning and mapping of underground infrastructures is a significant development in tunnel profiling, 3D referencing and gyro/laser surveying; a key service offered by the results of this project. The underground positioning relies on a network of satellites placed to surround an area of interest, with a range of up to 2km through soil or rock with accuracy better than 3%, enabling accurate positioning of underground assets. The robotic mapping system has the capabilities to accurately map tunnels, pipes and conduits, in detail and sequentially transfer the data collected into popular engineering CAD systems. A specialized military grade inertial referencing system (IRS) linked to multiple scanners provides high precision profiling while measuring roughness, deflection, ovality and positioning. The IRS component is linked to multiple laser scanners supplying high precision profiling while being driven forward. Laser scanning collects hundreds of data points per second linked to an accurate position through the IRS. All data is collected to on-board computer hard drives and transferred to the engineering office via memory storage systems or directly by wireless networks set up within the pipeline. Combining sectional scans with positioning and altitude data in real time creates 3D maps for surface referencing, a valuable service for pinpointing underground infrastructure problem locations in relation to surface features enabling informed risk management decisions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".