Design and Analysis of a Pose Estimator for Quadrotor MAVs With Modified Dynamics and Range Measurements
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Bibliographic record
Abstract
This paper presents the design and analysis of a pose estimator for quadrotor micro aerial vehicles (MAVs). The proposed design uses the dynamic model of the quadrotor with aerodynamic effects and uses the extended Kalman filter (EKF) formulation for state estimation. Range measurements to known locations, inertial measurements and height measurements are used for the estimation task. The purpose of the study is to evaluate the performance of the estimator when navigating through a changing indoor setting. The study investigates the effect of changing number of rannge measurements, different geometrical arrangements of range sensors and changing availability of confident height information on the performance of the estimator. Performance of the estimator for each scenario is numerically analyzed. Finally a criteria is proposed for selecting the sensors, number of range measurements, geometric location of sensors which facilitates accurate position estimation using the proposed method.
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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