Attitude estimation for normal flight and collision recovery of a quadrotor UAV
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
A comparison of attitude estimation algorithms is performed to allow selection of an appropriate algorithm for a quadrotor collision recovery system. A Multiplicative Extended Kalman Filter (MEKF), an Unscented Kalman Filter (UKF), a complementary filter, an H∞ Filter, and adaptive varieties of the selected filters are chosen for comparison. The adaptive modifications to the estimation algorithms are developed to better estimate the attitude during a collision. The algorithms are compared in simulated normal flight as well as during a simulated collision in order to show which estimation algorithm provides the best quadrotor attitude estimate in all conditions. An approach to modify simulated Inertial Measurement Unit (IMU) data to match experimental data during a quadrotor collision is developed. The results show that slight improvements can be found using the adaptive algorithms and that overall, the UKF algorithms are found to outperform other estimators during regular flight and after a collision.
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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