Bibliographic record
Abstract
Building a representation of space and estimating a robot's location within that space is a fundamental task in robotics known as simultaneous localization and mapping (SLAM). This work examines the problem of solving SLAM in aquatic environments using an unmanned surface vessel under conditions that restrict global knowledge of the robots pose. These conditions refer specifically to the absence of a global positioning system to estimate position, a poor vehicle motion model, and the lack of a strong stable magnetic field to estimate absolute heading. These conditions can be found in terrestrial environments where the line of sight to overhead satellites is occluded by surrounding structures and local magnetic inference disrupts reliable compass measurements. Similar conditions are anticipated in extra-terrestrial environments such as on Titan where the lack of a global satellite network inhibits the use of traditional positioning sensors and the lack of a stable magnetic core limits the applicability of a compass. This work develops a solution to the SLAM problem that utilizes shore features coupled with information about the depth of the water column. Theoretical results are validated experimentally using an autonomous surface vehicle utilizing omnidirectional video and a depth sounder. Solutions are compared to ground truth obtained using GPS.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".