A Novel Approach for IMU Denoising using Machine Learning
Bibliographic record
Abstract
Inertial Measurement Unit (IMU) sensors are used predominantly in navigation systems. Micro-Electro-Mechanical Systems (MEMS) sensors have been introduced as a cost-effective lightweight IMU. However, MEMS IMU has larger stochastic errors that accumulate over time, causing navigation drifts. This issue is dealt with by fusing IMUs with Global Navigation Satellite System (GNSS) to obtain reliable navigation. This fusion setup fails to provide continuous, reliable navigation during GNSS outage scenarios due to IMU errors. So in this paper, we propose a novel approach to reduce the navigation drifts by removing IMU errors using Light Gradient Boosting Machine (LightGBM) Machine Learning algorithm. Unlike many other works that use high-end expensive IMU to train the model to denoise low-cost MEMS IMU, this paper uses Inverse Kinematics to obtain clean IMU training data from the Position, Velocity and Attitude (PVA) values estimated using Extended Kalman Filter (EKF) when the GNSS is available and reliable. The proposed method is tested in both simulation and real data sets under different GNSS outage durations. Results showed significant improvement in position, velocity and orientation estimation.
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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".