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An Autonomous Positioning Method for Drones in GNSS Denial Scenarios Driven by Real-Scene 3D Models.

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Summary

This study introduces a novel drone positioning method using 3D models and point cloud reconstruction, achieving high accuracy without Global Navigation Satellite Systems (GNSS). This technology offers precise autonomous navigation for drones in challenging environments.

Keywords:
autonomous positioningimage matchingreal-scene 3D modelthree-dimensional reconstruction

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Area of Science:

  • Robotics and Autonomous Systems
  • Computer Vision
  • Geospatial Technology

Background:

  • Drones require reliable positioning for military and civilian applications.
  • Global Navigation Satellite Systems (GNSS) are prone to interference and signal loss.
  • Enhancing drone positioning accuracy and independence from GNSS is crucial.

Purpose of the Study:

  • To develop a novel autonomous drone positioning system.
  • To achieve high positioning accuracy independent of GNSS.
  • To validate the system's performance in complex urban environments.

Main Methods:

  • Utilizing a real-scene 3D model and image point cloud reconstruction.
  • Segmenting 3D models into image and point cloud datasets.
  • Performing real-time image capture with a monocular camera.
  • Employing image matching for preliminary position estimation and 3D point cloud reconstruction.
  • Matching reconstructed point clouds with real-scene 3D point cloud data.
  • Applying pose estimation algorithms for coordinate acquisition.

Main Results:

  • Successful autonomous positioning of drones in complex urban settings.
  • Achieved remarkable positioning accuracy of up to 0.4 meters.
  • Demonstrated the efficacy of the 3D model and point cloud reconstruction approach.

Conclusions:

  • The proposed method offers a robust solution for GNSS-independent drone positioning.
  • High accuracy positioning is attainable even in challenging environments.
  • This technology has significant implications for drone autonomy and navigation safety.