All-time Infrared Vision-based Pose Estimation for Autonomous Berthing of Unmanned Surface Vehicles
Bibliographic record
Abstract
Autonomous berthing remains a key challenge for unmanned surface vehicles (USVs), especially in dynamic marine environments where traditional methods relying on GPS, wireless communication, or visual markers face limitations due to signal attenuation and lighting changes. This article proposes a vision-based robust berthing pose estimation framework that integrates infrared light arrays and ArUco fiducial markers, enabling accurate and real-time pose estimation. The YOLO-v5n berth object detection model has been fine-tuned on custom datasets for berthing scenarios, while TensorRT optimization ensures efficient deployment on embedded platforms. The system utilizes adaptive image processing techniques, including Otsu binarization, Canny edge detection, and centroid-based light center localization, to isolate infrared LEDs and extract their geometric features under different illumination conditions. The perspective n-point (PnP) algorithm was used to estimate USV’s relative pose to the light array. The framework has been validated through experiments under different conditions, providing a reliable solution and potential applications for USV autonomous berthing.
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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) | 0.001 | 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) | 0.001 | 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 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".