Long Range Underwater Localization and Navigation using Gravity-Based Measurements
Bibliographic record
Abstract
This paper reports on work to assess the feasibility of gravity-based long range underwater navigation and localization. As a first step, this is explored in simulations with RAO-Blackwellized particle filter simultaneous localization and mapping (SLAM). When implemented on an autonomous underwater vehicle it can operate submerged for extended periods without the use of an active sensor, thus widening the variety of AUV missions. Additionally, this work applies information theory to navigate through a region such that the SLAM data association, and thus the localization, performance is improved. The results also indicate that characteristic values for a region can be used as a SLAM metric for the region. Combining the characteristic value with information theory techniques improves the localization performance at extended ranges and is a first step towards long range underwater localization using gravimeters. Future work will optimize the particle filter, explore more sophisticated loop closures as well as hardware-in-the loop tests.
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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 | 0.000 | 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".