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A Deep Learning Approach To Coarse Robot Localization
Bettaieb, Luc Alexandre

2017, Master of Sciences (Engineering), Case Western Reserve University, EECS - Electrical Engineering.
This thesis explores the use of deep learning for robot localization with applications in re-localizing a mislocalized robot. Seed values for a localization algorithm are assigned based on the interpretation of images. A deep neural network was trained on images acquired in and associated with named regions. In application, the neural net was used to recognize a region based on camera input. By recognizing regions from the camera, the robot can be localized grossly, and subsequently refined with existing techniques. Explorations into different deep neural network topologies and solver types are discussed. A process for gathering training data, training the classifier, and deployment through a robot operating system (ROS) package is provided.
Wyatt Newman (Advisor)
Murat Cavusoglu (Committee Member)
Gregory Lee (Committee Member)
120 p.

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Bettaieb, L. (2017). A Deep Learning Approach To Coarse Robot Localization. (Electronic Thesis or Dissertation). Retrieved from https://etd.ohiolink.edu/

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Bettaieb, Luc. "A Deep Learning Approach To Coarse Robot Localization." Electronic Thesis or Dissertation. Case Western Reserve University, 2017. OhioLINK Electronic Theses and Dissertations Center. 15 Dec 2017.

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Bettaieb, Luc "A Deep Learning Approach To Coarse Robot Localization." Electronic Thesis or Dissertation. Case Western Reserve University, 2017. https://etd.ohiolink.edu/

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