Factor Graph Optimization for Flexibly Modeled INS/GPS Navigation in Graphical State-Space
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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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".