Sensor Fusion for Autonomous Outdoor Navigation using Neural Networks

Abstract

For many navigation tasks, a single sensing modality is sufficiently rich to accomplish the desired motion control goals; for practical autonomous outdoor navigation, a single sensing modality is a crippling limitation on what tasks can be undertaken. In the research detailed in this paper, we open the door for a whole new suite of real time autonomous navigation tasks previously unattainable. Using neural networks, including a neural network paradigm particularly well suited to sensor fusion, and Carnegie Mellon University's HMMWV (High Mobility Multi-Wheeled Vehicle) off-road military ambulance, we have successfully performed simulated and real world navigation tasks that required the use of multiple sensing modalities.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1995
Accession Number
ADA293563

Entities

People

  • Ian L. Davis

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Energy and Power Technologies
  • Ground and Sea Platforms
  • Materials and Manufacturing Processes
  • Sensors
  • Space

DTIC Thesaurus Topics

  • Autonomous Navigation
  • Autonomous Systems
  • Charge Coupled Devices
  • Collision Avoidance
  • Computing System Architectures
  • Control Systems
  • Failure Mode And Effect Analysis
  • Guidance
  • Motion Planning
  • Navigation
  • Network Architecture
  • Neural Networks
  • Robotics
  • Robots
  • Sensor Fusion
  • Simulators
  • Video Images

Fields of Study

  • Computer science

Readers

  • Computer Vision.
  • Neural Network Machine Learning.
  • Unmanned Aerial System (UAS) Autonomous Capabilities and Mission Reconnaissance.

Technology Areas

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