Large Scale Structure From Motion for Autonomous Underwater Vehicle Surveys

Abstract

Our ability to image extended underwater scenes is severely limited by attenuation and backscatter. Generating a composite view from multiple overlapping images is usually the most practical and flexible way around this limitation. In this thesis we look at the general constraints associated with imaging from underwater vehicles for scientific applications-low overlap, non-uniform lighting and unstructured motion--and present a methodology for dealing with these constraints toward a solution of the problem of large area 3D reconstruction. Our approach assumes navigation data is available to constrain the structure from motion problem. We take a hierarchical approach where the temporal image sequence is broken into subsequences that are processed into 3D reconstructions independently. These submaps are then registered to infer their overall layout in a global frame. From this point a bundle adjustment refines camera and structure estimates. We demonstrate the utility of our techniques using real data obtained during a SeaBED AUV coral reef survey. Test tank results with ground truth are also presented to validate the methodology.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2004
Accession Number
ADA431208

Entities

People

  • Oscar R. Pizarro

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

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

DTIC Thesaurus Topics

  • Accuracy
  • Autonomous Underwater Vehicles
  • Computational Complexity
  • Computational Science
  • Computer Vision
  • Detectors
  • Geometry
  • Image Processing
  • Kalman Filters
  • Light Sources
  • Mathematical Filters
  • Measurement
  • Navigation
  • Reliability
  • Remotely Piloted Vehicles
  • Seabed
  • Unmanned Vehicles

Readers

  • Coastal Oceanography
  • Computational Modeling and Simulation
  • Computer Vision.

Technology Areas

  • AI & ML