VGPNet: A Vision-Aided GNSS Positioning Framework With Cross-Channel Feature Fusion for Urban Canyons
Bibliographic record
Abstract
Global Navigation Satellite System (GNSS) positioning in urban canyons is severely degraded by non-line-of-sight (NLOS) reception caused by building obstructions and signal reflections. Conventional approaches typically treat NLOS observations as unreliable and rely on classifying and excluding them. However, these classification-exclusion methods are highly sensitive to misclassification, potentially discarding line-of-sight (LOS) observations or valuable information embedded in NLOS observations. To overcome these limitations, this paper proposes a vision-aided GNSS positioning (VGPNet) framework that integrates GNSS observations with sky-pointing fisheye imagery to enhance positioning accuracy via deep contextual feature sensing. Unlike existing classification-exclusion based approaches, VGPNet preserves all satellite observations and leverages a deep fusion model to assign adaptive per-satellite weights and bias corrections, thus making full use of both LOS and NLOS observations. Central to VGPNet is a novel cross-channel feature fusion module, which projects both visual and GNSS features into a shared latent space. This enables robust and adaptive estimation of satellite-specific weights and biases. Extensive experimental evaluations in urban canyon scenarios confirm that VGPNet substantially outperforms existing state-of-the-art approaches in terms of positioning accuracy. The code is available at https://github.com/hu-xue/VGPNet.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".