Convolutional neural networks and particle filter for UAV localization
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
Unmanned aerial vehicles (UAV) are now used in a large number of applications. In order to accomplish autonomous navigation, UAVs must be equipped with robust and accurate localization systems. Most localization solutions available today rely on global navigation satellite systems (GNSS). However, such systems are known to introduce instabilities as a result of interference. More advanced solutions now use computer vision. While deep learning has now become the state-of-the-art in many areas, few attempts were made to use it for localization. In this paper, we present an entirely new type of approach based on convolutional neural networks (CNN). The network is trained with a new purpose-built dataset constructed using publicly available aerial imagery. Features extracted with the model are integrated in a particle filter for localization. Initial validation using real-world data, indicated that the approach is able to accurately estimate the localization of a quadcopter.
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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