A Modular Windows-Based Intelligent API for Traceable Drone Positioning Using UWB-OptiTrack Fusion and AI-Based Residual Learning

• 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:

  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:

  3 September 2025 - 5 September 2025