DeepCovPG:Deep-Learning-based Dynamic Covariance Prediction in Pose Graphs for Ultra-Wideband-Aided UAV Positioning
Bibliographic record
Abstract
In unmanned aerial vehicle (UAV) navigation, achieving high positioning accuracy is crucial but can be hindered by dynamic environmental uncertainties. This paper introduces DeepCovPG, a novel framework that leverages deep learning and Ultra-Wideband (UWB) technology to enhance positioning precision significantly. At its core, DeepCovPG incorporates a novel neural network architecture, combining Variational Autoencoder (VAE) with Long Short-Term Memory (LSTM) network, to refine UWB range data by noise reduction and dynamic covariance prediction. This approach integrates a dynamic covariance model within the pose graph optimization process, diverges from conventional static uncertainty approaches, enhancing adaptability to environmental shifts and measurement errors. Tested across various settings, including indoor spaces and urban landscapes, DeepCovPG demonstrated a significant 51% reduction in Root Mean Square Error (RMSE) and substantial Mean Absolute Error (MAE) improvements over traditional methods, proving its effectiveness in tackling signal interference and navigational challenges for reliable UAV positioning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".