Adaptive Gradient-Descent Extended Kalman Filter for Pose Estimation of Mobile Robots with Sparse Reference Signals
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Bibliographic record
Abstract
This paper proposes a novel extended Kalman filter (EKF) along with its adaptive variant for effective magnetic, angular rate and gravity (MARG) sensor-only pose estimation of mobile robots operated longer periods in reference-denied environments. First, a gradient-descent orientation-based EKF framework is derived, which formulates the MARG-based pose propagation with both bandpass-filtered and bias compensated external acceleration signals. The proposed approach uses two correction signals beside the orientation update, namely, virtual observations and sparse reference signals are incorporated in the state correction. Next, the instantaneous dynamics is characterized by accelerometer/gyroscope signals-based measures and an adaptive strategy is derived for real-time tuning of EKF parameters. The algorithm is fine tuned in an optimization framework on an appropriate database. This database of ground truth and raw MARG measurements contains 16 robot motion scenarios, where both slow motions and agile maneuvers are performed on different terrains. The conducted analysis highlights that the proposed algorithms outperform the standard approaches, moreover, the adaptive strategy further improves the performance by 13%. The comprehensive performance evaluation demonstrates the efficacy of the new algorithms, thereby these robust approaches are proposed in environments characterized by sparse reference measurements.
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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