UAV navigation in GPS-denied environment using particle filtered RVL
Bibliographic record
Abstract
Unmanned aerial vehicles have become widespread in today’s world and are used for applications ranging from real estate marketing and bridge inspection to defense and military applications. These applications have in common some form of autonomous navigation that requires a good localization capability at all time. Most UAV are using a combination of global navigation satellite systems (GNSS) and inertial measurement unit (IMU) to perform this task. Unfortunately, GNSS are subject to signal unavailability and all sorts of interference impeding on the ability of the UAV to self-localize. In this paper, we propose a new algorithm to perform localization in GNSS-denied environments by using a relative visual localization technique. We developed a new measure based on the use of local feature points extracted with ORB to estimate the likelihood of a previously captured image to have been taken in a position close to the current UAV location. The measure is embedded in a particle filter in which IMU data is used in order to reduce the number of images we need to analyze to perform localization. The resulting method have shown significant improvement in both accuracy and execution time in comparison to previous approaches.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".