Real-time side scan image generation and registration framework for AUV route following
Bibliographic record
Abstract
Memorial University is in the development stages of a Qualitative Navigation System (QNS) to be deployed on the Memorial Explorer AUV. This system will allow localization and path following along a trained route without the necessity of a globally referenced position estimate. Previous QNS work has been on terrestrial robots using optical images. Our main challenge lies in utilization of side scan sonar as the imaging medium, as this type of sonar is prevalent on AUVs and provides much better range and coverage than optics in water. To achieve this, a sonar image processing and registration framework has been developed. To be useful such a framework should be fully-autonomous, robust, and operate in real-time, where real-time operation is defined as the ability to process, register and localize data at the rate it is collected, or faster. In this paper we describe our framework for processing sonar data, generating image tiles, extracting unique features and localizing against a reference set. We also present some results of this system based on raw sonar input data collected by the AUV.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".