Large-Area Visually Augmented Navigation for Autonomous Underwater Vehicles

Abstract

This thesis describes a vision-based, large-area, simultaneous localization and mapping (SLAM) algorithm that respects the low-overlap imagery constraints typical of autonomous underwater vehicles (AUVs) while exploiting the inertial sensor information that is routinely available on such platforms. We adopt a systems-level approach exploiting the complementary aspects of inertial sensing and visual perception from a calibrated pose-instrumented platform. This systems-level strategy yields a robust solution to underwater imaging that overcomes many of the unique challenges of a marine environment (e.g., unstructured terrain, low-overlap imagery, moving light source).

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2005
Accession Number
AD1162736

Entities

People

  • Ryan M. Eustice

Organizations

  • Michigan State University

Tags

Communities of Interest

  • Autonomy
  • Biomedical
  • Sensors
  • Space

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Autonomous Navigation
  • Autonomous Systems
  • Autonomous Underwater Vehicles
  • Computational Science
  • Computer Vision
  • Detectors
  • Guidance
  • Image Processing
  • Inertial Navigation
  • Information Processing
  • Information Science
  • Information Systems
  • Kalman Filters
  • Mathematical Filters
  • Motion Planning
  • Robots
  • Seabed
  • Simultaneous Localization And Mapping
  • Surveys
  • World Geodetic System

Readers

  • Acoustical Oceanography.
  • Aerospace Test and Evaluation
  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.