Feasibility Study of a Vision-Based Landing System for Unmanned Fixed-Wing Aircraft

Abstract

Successful landing of an autonomous unmanned aerial vehicle requires a high degree of accuracy and efficient, real-time processing. This research applies systems engineering concepts to investigate the feasibility of applying computer vision techniques and visual feedback in the control loop for an autonomous system. This thesis examines the framework and performance of an algorithm designed to detect and track a runway in images captured from a camera onboard an aircraft during the final approach and landing stages of flight. Using a series of image processing techniques to localize the runway and the Hough transformation for line detection, the algorithm is capable of detecting the edges of a runway with over 96 percent accuracy through 3000 test images. The operating conditions for this algorithm include any scenario in which visual flight rules apply. Additionally, the system will perform with runways that comply with Federal Aviation Administration regulations. Future applications of this algorithm should include aircraft attitude and pose estimation as well as full integration into an autonomous aircraft control system.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2017
Accession Number
AD1046476

Entities

People

  • Tyler B. Mccarthy

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Air Platforms
  • Autonomy
  • Ground and Sea Platforms
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Aircrafts
  • Airframes
  • Artificial Intelligence
  • Autonomous Systems
  • Computer Graphics
  • Computer Vision
  • Control Systems
  • Fixed Wing Aircraft
  • Global Positioning Systems
  • Image Processing
  • Inertial Navigation
  • Inertial Navigation Systems
  • Navigation
  • Systems Engineering
  • Unmanned Aerial Systems
  • Unmanned Aerial Vehicles
  • Unmanned Systems

Readers

  • Aviation Safety and Air Traffic Management
  • Computer Vision.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • Autonomy