Mapping for control in an underwater environment using a dynamic inverse-sonar model
Bibliographic record
Abstract
Autonomous Underwater Vehicles (AUVs) are commonly used in oceanographic applications such as seafloor survey and underwater iceberg profiling. During the surveys, the vehicles are intended to follow the variation of the seafloor or iceberg surface at a constant stand-off distance in order to maintain a consistent sensor footprint. Mechanical scanning sonar are usually chosen for measuring the distance from the vehicle to the object. Due to the wide beamwidth of the sonar, the uncertainty from the unknown direction of the received echo will affect the accuracy of the resulting environmental map. On the mobile robots, a static inverse-sonar model is introduced to compensate for such uncertainty for range finders. As an improvement, we present a dynamic inverse-sonar model accounting for the trend of the surveying environment, i. e. the terrain elevation. A vehicle-attached occupancy map is introduced for estimating the terrain elevation, while a global occupancy map is created to present the surrounding environment. The proposed technique is first simulated in mapping a vertical underwater column. The resulting global map is found to be more accurate in presenting the original underwater features compared to the results from a static inverse-sonar model. The technique is further applied to a set of sonar-data collected from an iceberg profiling trial.
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 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 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".