Object Detection with Deep Learning to Accelerate Pose Estimation for Automated Aerial Refueling

Abstract

RPAs cannot currently refuel during flight because the latency between the pilot and the aircraft is too great to safely perform aerial refueling maneuvers. However, an AAR system removes this limitation by allowing the tanker to directly control the RP A. The tanker quickly finding the relative position and orientation (pose) of the approaching aircraft is the first step to create an AAR system. Previous work at AFIT demonstrates that stereo camera systems provide robust pose estimation capability. This thesis first extends that work by examining the effects of the cameras' resolution on the quality of pose estimation. Next, it demonstrates a deep learning approach to accelerate the pose estimation process. The results show that this pose estimation process is precise and fast enough to safely perform AAR.

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Document Details

Document Type
Technical Report
Publication Date
Mar 26, 2020
Accession Number
AD1095514

Entities

People

  • Andrew T. Lee

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Air Platforms
  • Energy and Power Technologies
  • Sensors

DTIC Thesaurus Topics

  • Air Force
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Automata Theory
  • Computational Science
  • Computer Stereo Vision
  • Computer Vision
  • Computers
  • Convolutional Neural Networks
  • Global Positioning Systems
  • Image Processing
  • Machine Learning
  • Neural Networks
  • Recurrent Neural Networks
  • Tanker Aircraft
  • Three Dimensional
  • United States Government

Readers

  • Aerospace logistics and air mobility.
  • Neural Network Machine Learning.
  • Robotics and Automation.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Neural Networks