Research on Vision-Based Underwater Polarization Navigation
Bibliographic record
Abstract
This study investigates an vision-based polarization navigation acquisition structure and its corresponding imaging polarization navigation algorithm, aiming to enhance the accuracy of bionic polarization navigation, reduce the navigation failure rate, and simplify the structure of the polarization collector. A rotational simple polarizer structure is proposed herein, and an imaging polarization collector is designed using a multi-camera lens system. The solar azimuth angle in the carrier coordinate system is obtained based on dynamic threshold segmentation of polarization degree valleys and the K-Means clustering algorithm, while the RANSAC robust regression method is employed for angle sequence fitting. Additionally, this study extends the research on the propagation characteristics of polarized light in underwater environments and proposes a correction algorithm suitable for underwater polarized image processing. Finally, by utilizing the current year, date, and realtime, the solar azimuth angle in the navigation coordinate system at the current moment is calculated. Using the solar azimuth angle as an intermediate bridge, the relative relationship between the carrier coordinate system and the navigation coordinate system is determined, thereby completing the navigation and orientation of the carrier. Experimental results demonstrate that the proposed algorithm exhibits excellent orientation performance both in clear weather and underwater environments.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".