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Real-Time Compact Environment Representation for UAV Navigation.

Kaitao Meng1, Deshi Li1,2, Xiaofan He1

  • 1Electronic Information School, Wuhan University, Wuhan 430072, China.

Sensors (Basel, Switzerland)
|September 5, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces new algorithms for unmanned aerial vehicles (UAVs) to create compact environment models. These methods improve navigation in unknown areas by efficiently representing obstacles.

Keywords:
compact environment representationkernel density estimationobstacle sensingunmanned aerial vehicle

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Unmanned aerial vehicles (UAVs) require efficient environment representation for navigation in unknown spaces due to onboard storage limitations.
  • Accurate and compact environmental modeling is challenging due to unknown obstacle shapes and complex data processing.

Purpose of the Study:

  • To develop novel algorithms for real-time, compact environment representation for UAV navigation.
  • To address the challenges of unknown obstacle geometry and data processing efficiency in UAV mapping.

Main Methods:

  • A vertical strip extraction algorithm analyzes probability density functions of normalized disparity values for obstacle segmentation using adaptive sliding windows.
  • A plane adjustment algorithm represents obstacle surfaces as polygonal prisms, minimizing redundant data.
  • Integration of these algorithms enables real-time conversion of depth sensor data into multi-layer polygonal prism models.

Main Results:

  • The proposed algorithms successfully convert depth sensor data into compact, multi-layer polygonal prism models.
  • A drone platform equipped with a depth sensor was developed for real-world model construction.
  • Experimental results show superior precision and storage efficiency compared to existing methods.

Conclusions:

  • The developed vertical strip extraction and plane adjustment algorithms provide an effective solution for real-time, compact environment modeling for UAVs.
  • The proposed scheme enhances UAV navigation capabilities in unknown environments by improving environmental representation accuracy and reducing storage requirements.