A Modular Windows-Based Intelligent API for Traceable Drone Positioning Using UWB-OptiTrack Fusion and AI-Based Residual Learning |
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| Ihtisham Ul Haq, Luigi D’Alfonso, Giuseppe Fedele, Francesco Lamonaca |
- Abstract:
- Accurate and traceable drone positioning is crucial for autonomous aerial navigation, especially in GNSS-denied environments. Traditional approaches using Ultra-Wideband (UWB) sensors or Kalman Filters (KF) struggle with multipath interference, non-line-of-sight, and environmental uncertainties, and are often limited to Linux-based Application Programming Interfaces (APIs). This work presents an innovative framework based on: a novel modular Windows-based API for real-time drone positioning, the integration of Kalman filter for optimal multi-sensor data fusion and trajectory smoothing, AI-driven residual learning to correct systematic estimation errors, and metrology-compliant uncertainty modeling. The system enables real-time swarm deployment and pose-aware feedback using an auxiliary vision based positioning system (OptiTrack) and UWB data. A feedforward neural network compensates for residual errors in Kalman-filtered trajectories, while Monte Carlo simulations establish traceable 95% confidence intervals. Experimental tests show that the proposed framework reduces RMSE by over 40% across axes, with strong regression accuracy greater than 94%.
- Download:
- IMEKO-TC6-2025-061.pdf
- DOI:
- 10.21014/tc6-2025.061
- Event details
- IMEKO TC:
- TC6
- Event name:
- TC6 M4Dconf2025
- Title:
2025 IMEKO TC-6 International Conference on Metrology and Digital Transformation
- Place:
- Benevento, ITALY
- Time:
- 03 September 2025 - 05 September 2025