Loosely coupled GPS/INS integration with snap to road for low-cost land vehicle navigation: EKF-STR for low-cost applications
Why this work is in the frame
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Bibliographic record
Abstract
Nowadays, the availability of the vehicle position gets more and more important. The use of the Global Positioning System (GPS) receiver has solved this problem. Nevertheless, this system could suffer from availability of the minimum number of visible satellite, especially in harsh environment. Thus, complementary system, such us Inertial Navigation System (INS) comes to help the GPS in order to guarantee the availability of the position in these environments. Nevertheless, low-cost Microelectromechanical System (MEMS) based INS integrated with the GPS has shown weak performances even in case of using Kalman filtering. To deal with this problem, this paper proposes a new approach based on loosely coupled GPS/INS integration using Extended Kalman Filter (EKF) and aided by the map matching technique Snap To Road (STR). Experimental tests of EKF aided by STR tehcnique have shown better performances than EKF alone even in harsh environment.
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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 | 0.000 | 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 it