Type of Document Master's Thesis Author Torok, Matthew M. Author's Email Address email@example.com URN etd-05032012-104351 Title Autonomous Sample Collection Using Image-Based 3D Reconstructions Degree Master of Science Department Mechanical Engineering Advisory Committee
Advisor Name Title Golparvar-Fard, Mani Committee Co-Chair Kochersberger, Kevin Bruce Committee Co-Chair Sandu, Corina Committee Member Keywords
- Building Crack Detection
- Autonomous Sample Collection
- Image-based 3D Reconstruction
Date of Defense 2012-04-26 Availability unrestricted AbstractSample collection is a common task for mobile robots and there are a variety of manipulators
available to perform this operation. This thesis presents a novel scoop sample collection
system design which is able to both collect and contain a sample using the same hardware.
To ease the operator burden during sampling the scoop system is paired with new
semi-autonomous and fully autonomous collection techniques. These are derived from data
provided by colored 3D point clouds produced via image-based 3D reconstructions. A custom
robotic mobility platform, the Scoopbot, is introduced to perform completely automated
imaging of the sampling area and also to pick up the desired sample. The Scoopbot is wirelessly
controlled by a base station computer which runs software to create and analyze the
3D point cloud models. Relevant sample parameters, such as dimensions and volume, are
calculated from the reconstruction and reported to the operator. During tests of the system
in full (48 images) and fast (6-8 images) modes the Scoopbot was able to identify and
retrieve a sample without any human intervention. Finally, a new building crack detection
algorithm (CDA) is created to use the 3D point cloud outputs from image sets gathered by
a mobile robot. The CDA was shown to successfully identify and color-code several cracks
in a full-scale concrete building element.
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