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Overview obstacle maps for obstacle-aware navigation of autonomous drones.

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Autonomous drones can now map unknown outdoor areas quickly using onboard computers. This vision-based system enables rapid obstacle mapping for applications like search and rescue, enhancing drone autonomy.

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Area of Science:

  • Robotics
  • Computer Vision
  • Geospatial Mapping

Background:

  • Autonomous drone deployment in unknown environments is challenging.
  • Lack of prior maps limits drone utility in scenarios like search and rescue.
  • Onboard computation for real-time mapping is crucial for enhanced autonomy.

Purpose of the Study:

  • To demonstrate the feasibility of autonomous drone deployment in unknown outdoor environments.
  • To develop a vision-based system for rapid obstacle map generation using onboard computation.
  • To enhance drone autonomy for tasks in unmapped areas.

Main Methods:

  • A two-step vision-based mapping approach: overview flight for sparse mapping via photogrammetry, followed by georeferencing, mesh densification, and Octomap conversion.
  • Utilizing onboard computation for near real-time generation of obstacle maps ( size in ).
  • Quantitative evaluation of map accuracy and trajectory planning, alongside experimental validation of safe navigation.

Main Results:

  • Successful generation of an obstacle map in near real-time on the drone's onboard computer.
  • Demonstrated feasibility of autonomous deployment and mapping in unknown outdoor environments.
  • Validated accuracy of the generated map and safety of drone navigation within the mapped area.

Conclusions:

  • The proposed vision-based mapping approach enables autonomous drones to rapidly create obstacle maps in unknown environments using only onboard computation.
  • This system significantly enhances drone autonomy for critical applications such as search and rescue.
  • The method allows sufficient time for drones to perform other tasks within the area of interest during the same mission.