PPT Slide

3D Mapping Methodology

Stanford University School of Engineering

Department of Mechanical Engineering

XX February 2000

3D Stitching

3D Mesh

• Known mesh vs. camera position
• Quantifiable object measurements with known error

Vehicle/Camera position readings from inertial positioning system

multiple mesh/?position?inputs

3D map

Known error in every?possible measurement?quantified and optimized

Error?Quantification Algorithm

Jeff’s Proposed Contribution

Error ?Reduction Algorithm

Feature-based mesh stitching algorithm

Camera position based mesh stitching algorithm

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Mention:

• 1 vs. 2 cameras (current limits in internal position knowledge)
  • Doppler based navigation (1cm)

• 2 cameras gets you down to machining errors mils (will show why this important in geometrical derivation next)

• Individual Camera Calibration
  • Model development from calibration process
    • Calculated intrinsic and extrinsic parameters fed into stereo? pipeline for mesh building.
  • Distortion free images (de-warped) (done)

• Stereo Pipeline
  • Develop calibration process to characterize errors based on mesh-building algorithm.
  • Laser assist?
  • Calibration process would also allow for using other “pipelines”

• Mapping/mesh-stitching
  • Calibrate system (ROV) wide errors using navigation data and correcting using mesh stitching algorithms.