Factor Graph Optimization for Flexibly Modeled INS/GPS Navigation in Graphical State-Space
Why this work is in the frame
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Bibliographic record
Abstract
This article investigates loosely coupled inertial navigation system/global positioning system (INS/GPS) integration for land vehicle navigation. To achieve navigation with higher accuracy and lower computational complexity, we present an integration solution using factor graph optimization (FGO) based on the graphical state-space model (GSSM). This solution is referred to as GSSM-FGO. Compared with traditional methods, the unique specialty of our work lies in both modeling and problem-solving aspects under the assumption of calibration parameter invariance. Specifically, we suggest that the time-series state-space model is not always suitable for widely existing constant calibration parameters. Thus, we propose GSSM as a more flexible and accurate state description by extracting the constant states as singular nodes. The FGO is adopted to manage this novel graphical model, while traditional filter-based algorithms fail when faced with the cyclic model structure. The universality of our approach is validated through a real-world land vehicle navigation dataset, featuring four distinct-grade inertial measurement units. Compared to the methods based on extended Kalman filter and FGO with the traditional state-space model, our approach demonstrates a substantial enhancement in estimation accuracy and computational speed.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it