Neural Networks for Real-Time Terrain Typing.

Abstract

Many robotics tasks require an ability to determine quickly the nature of the terrain surrounding the robot. In cross country navigation in particular, the robot needs to know where the vegetation is and where the hard obstacles are. I have developed a general system which has successfully allowed real-time terrain typing in the NavLab II autonomous vehicle. This system and training paradigm are based on standard neural network technology and allow the robot to learn arbitrary non-linear mappings from color and texture space to terrain space.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1995
Accession Number
ADA293569

Entities

People

  • Ian L. Davis

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Air Platforms
  • Autonomy
  • Ground and Sea Platforms
  • Space

DTIC Thesaurus Topics

  • Autonomous Navigation
  • Classification
  • Computer Vision
  • Computing System Architectures
  • Ground Vehicles
  • Guidance
  • High Resolution
  • Image Classification
  • Image Segmentation
  • Information Processing
  • Motion Planning
  • Navigation
  • Neural Networks
  • Robotics
  • Robots
  • Test Sets
  • Vehicles

Readers

  • Computer Vision.
  • Neural Network Machine Learning.
  • Robotics and Automation.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Neural Networks
  • Autonomy
  • Space
  • Space - Spacecraft Maneuvers