DETERMINATION OF UAS TRAJECTORY IN A KNOWN ENVIRONMENT FROM FPV VIDEO
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| 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