Side-scan sonar image registration for AUV navigation
Bibliographic record
Abstract
The ability of an AUV to navigate an underwater environment precisely and for an extended period depends on its effectiveness at making accurate observations regarding its location and orientation. An AUV platform equipped with a side-scan sonar system has the potential to register the current sonar image with previously captured images for the purpose of obtaining information about the vehicle's pose. Image registration is a procedure which transforms images viewed from different perspectives into a single coordinate system. The significance of using image registration techniques in a surveying or monitoring context comes from the fact that the registration parameters could provide the AUV with an indication of the discrepancy between its expected and observed pose vectors. As such, image registration provides feedback which can be used to compensate for drift in inertial sensors or to provide a standalone navigation solution in the event that the inertial navigation system fails. In order for image registration to provide an effective means for feedback a number of requirements on the performance of the image registration method employed must be met. Not only must the method be accurate in the face of possible image variations, but it must operate in real-time using the limited computing resources available within an AUV. In this paper, a number of key image registration techniques are applied to side-scan sonar images. These techniques include those based on the maximization of mutual information, log-polar cross-correlation, the Scale-Invariant Feature Transform (SIFT), and phase correlation. The performance of these techniques is assessed based on a number of metrics including execution time and registration accuracy. The challenges introduced by side-scan sonar imaging systems which degrade the performance of image registration are also discussed in detail.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.005 | 0.004 |
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".