Disturbance Observer and Depth Enhanced Visual-Inertial Navigation System For Multi-rotor MAVs: An Observability Analysis
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Bibliographic record
Abstract
This paper proposes a new filtering-based depth enhanced visual internal navigation system (DE-VINS) with external disturbance observation. This filter resolves the drifting and degraded performance of drag force model VINS filters at hovering conditions and during the existence of external disturbances. A theoretical nonlinear observability analysis is performed to verify the filter design. The performance of the proposed DE-VINS is investigated through two sets of numerical simulations using a Matlab simulator and compared against the state-of-the-art drag force VINS filters. The results show improved performance of the DE-VINS in terms of estimation accuracy and consistency at zero-velocity flight (hovering) during the existence of external disturbances while estimating the magnitude and direction of the disturbance force.
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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