Terrain Understanding for Robot Navigation

Abstract

This paper presents a method to forecast terrain trafficability from visual appearance. During training, the system identifies a set of image chips (or exemplars) that span the range of terrain appearance. Each chip is assigned a vector tag of vehicle-terrain interaction characteristics that are obtained from on-board sensors and simple performance models, as the vehicle traverses the terrain. The system uses the exemplars to segment images into regions, based on visual similarity to the terrain patches observed during training, and assigns the appropriate vehicle-terrain interaction tag to them. This methodology will therefore allow the online forecasting of vehicle performance on upcoming terrain. Currently, we are using fuzzy c-means clustering and exploring a number of different features for characterizing the visual appearance of the terrain.

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Document Details

Document Type
Technical Report
Publication Date
Oct 29, 2007
Accession Number
ADA518459

Entities

People

  • Gary Witus
  • Robert E. Karlsen

Organizations

  • United States Army Tank Automotive Research, Development and Engineering Center

Tags

Communities of Interest

  • Air Platforms
  • Autonomy
  • Human Systems
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Autonomous Navigation
  • Cameras
  • Coding
  • Cognition
  • Computer Vision
  • Data Processing
  • Data Sets
  • Detectors
  • Distance Learning
  • Image Processing
  • Image Reconstruction
  • Information Science
  • Navigation
  • Robot Navigation
  • Robots
  • Supervised Machine Learning

Fields of Study

  • Computer science

Readers

  • Military Science
  • Neural Network Machine Learning.
  • Pavement Materials Engineering.

Technology Areas

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