Conditional Probabilistic Relative Visual Localization for Unmanned Aerial Vehicles
Why this work is in the frame
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Bibliographic record
Abstract
Unmanned aerial vehicles (UAV) are now used for a large number of applications in everyday life. These applications require autonomous navigation which is enabled by the self-localization solution integrated to the UAV. To perform self-localization, most UAVs are relying on a series of sensors combined with a global navigation satellite system (GNSS) in a sensor fusion framework. However, GNSS are using radio signals which are subjected to a large range of outages and interferences. This paper presents a relative visual localization (RVL) approach for GPS-denied environments using a down-facing 2D monocular camera and an inertial measurement unit (IMU). The solution is embedded in an adapted particle filter and use feature points to match images and estimate the localization of the UAV. A new conditional RVL measure is developed in order to leverage spare computation resources available during the data collection when the UAV is still receiving a GNSS signal. An evaluation of six feature point extraction methods is performed using real-world data while varying the number of feature points extracted. The results are promising and the approach has shown to be more efficient and to have fewer limitations than similar approaches in the literature.
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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