Conditional Probabilistic Relative Visual Localization for Unmanned Aerial Vehicles
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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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".