Evaluation of a CNN-based Visual Place Recognition system for GPS-denied Navigation of VTOL Vehicles
Bibliographic record
Abstract
Current Vertical Take-Off and Landing (VTOL) systems rely mainly on Global Positioning System (GPS) for autonomous navigation. Due to the unreliability of GPS, the need for alternative methods has become significant. Among the alternative approaches, Visual Place Recognition (VPR) systems have taken prominence. The latest advancements of these VPR systems involve using deep neural networks, such as Convolutional Neural Nets (CNNs), to overcome the limitations of conventional feature-based systems. These VPR methods have been tested and validated primarily for ground-based datasets. However, to properly assess the suitability of those approaches in VTOL navigation, they need to be evaluated for aerial image data sets. This study evaluates the performance of a CNN-based VPR system against a conventional feature-based method for an aerial image dataset, focusing mainly on the systems' front-end. Furthermore, experimental validation of the CNN-based VPR system is conducted. The results suggest that it is a better addition to the navigation stack of a VTOL vehicle under GPS-denied situations.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".