Improved IMU/GNSS EKF Fusion Using XGBoost Machine Learning Algorithm
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
For years, Inertial Measurement Unit (IMU) and Global Positioning System (GPS) have been playing a crucial role in navigation systems. The emergence of inexpensive IMU sensors has offered a lightweight alternative, yet they suffer from larger errors that build up gradually, leading to drift errors in navigation. Traditionally, IMUs are combined with GPS to ensure stable and accurate navigation. However, this integration fails during challenging GPS environments. This work introduces an inventive approach to alleviate navigation drifts by eradicating IMU errors through the utilization of the Extreme Gradient Boosting (XGBoost) algorithm. Unlike traditional approaches relying on high-end IMUs, this paper utilizes Inverse Kinematics to obtain pseudo-clean IMU training data from PVA values estimated by the EKF when GPS signals are available. The proposed approach is evaluated using both simulated and real-world datasets across various GPS outage durations, demonstrating significant enhancements in PVA estimation.
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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 | 0.001 |
| 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) | 0.017 | 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 it