A FEASIBILITY STUDY ON USING ViSP’S 3D MODEL-BASED TRACKER FOR UAV POSE ESTIMATION IN OUTDOOR ENVIRONMENTS
Why this work is in the frame
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Bibliographic record
Abstract
Abstract. This paper presents a novel application of the Visual Servoing Platform’s (ViSP) for small UAV pose estimation in outdoor environments. Given an initial approximation for the camera position and orientation, or camera pose, ViSP automatically establishes and continuously tracks corresponding features between an image sequence and a 3D wireframe model of the environment. As ViSP has been demonstrated to perform well in small and cluttered indoor environments, this paper explores the application of ViSP for UAV mapping of outdoor landscapes and tracking of large objects (i.e. building models). Our presented experiments demonstrate the data obtainable by the UAV, assess ViSP’s data processing strategies, and evaluate the performance of the tracker.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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