Comparison of feature detection techniques for AUV navigation along a trained route
Why this work is in the frame
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Bibliographic record
Abstract
Autonomous underwater vehicles (AUV)s traversing a path will incur positional error drift over time while submerged. We are developing a route following system which is based upon features extracted from the seabed using sidescan sonar collected in a training phase. Through matching of sonar images, this system navigates over a path without the need for a continual global position estimate. At the core of this system is the need to reliably extract features and match images derived from the sonar. At our disposal is an array of algorithms which implement the OpenCV common interface for feature extraction and matching. Using pre-collected sets of data we compare the performance of several of these algorithms in the context of matching sonar image tiles. Our results compare the performance of various feature types over two common sets of data. The feature types tested include SIFT[12], SURF[3], MSER[13], STAR[1], ORB[15], and BRIEF[4].
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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