DETERMINATION OF UAS TRAJECTORY IN A KNOWN ENVIRONMENT FROM FPV VIDEO
Bibliographic record
Abstract
Abstract. This paper presents a novel self-localization method. The algorithm automatically establishes correspondence between the FPV video streamed from a UAS flying in a structured urban environment and its 3D model. The resulting camera pose provides a precise navigation solution in the densely crowded environment. Initially, Vertical Line Features are extracted from a streamed FPV video frame, as the camera is kept approximately leveled through a gimbal system. The features are then matched with Vertical Line Features extracted from a synthetic image of the 3D model. A space resection is performed to provide the EOPs for this frame. The features are tracked in the next frame, followed by an incremental triangulation. The main contribution of this paper lies in automating this process as an FPV video sequence typically consists of thousands of frames. Accuracies of the position and orientation parameters of the video camera and the validation checks of the estimated parameters are presented. Future work includes testing the method in real-time to determine latencies and reliability, and multi-directional field of view of the FPV video camera.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".