Algorithms and experiments for globally consistent mapping of underground passageway environments
Bibliographic record
Abstract
The problem addressed by this thesis is whether globally consistent mapping can be practically achieved for the underground mining industry with little to no infrastructure and no a priori knowledge of the environment. This thesis has specific application to an underground global positioning system (UGPS) research project that is currently underway at MDA Space Missions of Brampton, ON. Using developed methods and algorithms from the mobile robotics literature, a tailored mapping algorithm was constructed for the effective and accurate mapping of large scale passageway environments. Following the use of a simulated environment for both feasibility tests and algorithm validation, the developed algorithms were applied to three real data sets, each having unique characteristics. Two of the data sets were obtained from underground mines courtesy of Atlas Copco Rock Drills AB of Orebro, Sweden. A quantitative and qualitative analysis of the produced pose estimates and maps provided clear indications about where the developed algorithms perform well, and also identified possible areas of future research. Finally, the successful integration of the generated maps into MDA's localization research was achieved.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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 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".