PPT Slide

Proposed Research Contributionsand Corresponding Approach

Stanford University School of Engineering

Department of Mechanical Engineering

XX February 2000

• Develop error quantification algorithm for a 3D map generated from a 6 degree-of-freedom moving platform with rough camera position knowledge

  • Account for intra-mesh (camera and image geometry) and inter-mesh (rough camera position knowledge) errors and incorporate in final map parameters for input into analysis packages
• Develop mesh capturing methodology to reduce inter-mesh errors

  • Current hypothesis suggests the incorporation of multiple overlapping meshes and cross-over (Fleischer ë00) paths will reduce the known error for the inter-mesh stitching.

    • Utilize a combination of camera position knowledge and computer vision mesh ìzippingî techniques

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Eric recommended that we hunker down on the ground and do a tripod-type approach underwater

Position Knowledge
• Heading (magnetic and gyro compass)
• Roll and Pitch (inclinometer)
• Depth (pressure sensor)
• altimeter (sonar)

System level calibration characterizes error in positioning system

Stitching
• How precise can you get from vision ìzippingî (Turk and Levoy)
• How much influence can the navigation data have? Is it necessary?