Real-time side scan image generation and registration framework for AUV route following
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
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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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 | 0.000 |
| 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