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A Deep Learning Approach To Coarse Robot Localization

Bettaieb, Luc Alexandre

Abstract Details

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.

Recommended Citations

Citations

  • Bettaieb, L. A. (2017). A Deep Learning Approach To Coarse Robot Localization [Master's thesis, Case Western Reserve University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=case1493646936728041

    APA Style (7th edition)

  • Bettaieb, Luc. A Deep Learning Approach To Coarse Robot Localization. 2017. Case Western Reserve University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=case1493646936728041.

    MLA Style (8th edition)

  • Bettaieb, Luc. "A Deep Learning Approach To Coarse Robot Localization." Master's thesis, Case Western Reserve University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=case1493646936728041

    Chicago Manual of Style (17th edition)