Bibliographic record
Abstract
This paper describes a computationally viable localization technique for an autonomous underwater vehicle (AUV) that is used to carry visual inspection and nondestructive testing equipment inside large water conduits and tunnels without decommissioning the service. The localization technique is required to estimate the instantaneous location of the robot with sufficient accuracy for the control system of the robot in real time. The proposed technique features a sensor fusion framework that incorporates a monocular camera and an inertial navigation system (INS). Localization is carried out using a standard Lucas-Kanade algorithm which searches for a subset of matching pixels between two sequential images to estimate a motion vector for the time interval between the two images. The novelty of the proposed technique is in regards with the use of an INS to predict a rotation and translation vector between the two sequential images. This prediction is used to minimize the search region of the Lucas-Kanade algorithm and hence significantly reduce the computational load of the overall localization process. Experimental results on a special testbed verify that the proposed system not only reduces the computational load but also improves the accuracy since finding a false match in the minimized search region is unlikely.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".