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A Deep-Learning Approach to Evaluating the Navigability of Off-Road Terrain from 3-D Imaging

2017, Master of Sciences (Engineering), Case Western Reserve University, EECS - Computer and Information Sciences.

This work investigates a strategy for evaluating the navigability of terrain from 3-D imaging. Labeled training data was automatically generated by running a simulation of a mobile robot nai¨vely exploring a virtual world. During this exploration, sections of terrain were perceived through simulated depth imaging and saved with labels of safe or unsafe, depending on the outcome of the robot's experience driving through the perceived regions. This labeled data was used to train a deep convolutional neural network. Once trained, the network was able to evaluate the safety of perceived regions. The trained network was shown to be effective in achieving safe, autonomous driving through novel, challenging, unmapped terrain.
Wyatt Newman (Advisor)
Cenk Cavusoglu (Committee Member)
Michael Lewicki (Committee Member)
84 p.

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Pech, T. (2017). A Deep-Learning Approach to Evaluating the Navigability of Off-Road Terrain from 3-D Imaging. (Electronic Thesis or Dissertation). Retrieved from https://etd.ohiolink.edu/

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Pech, Thomas. "A Deep-Learning Approach to Evaluating the Navigability of Off-Road Terrain from 3-D Imaging." Electronic Thesis or Dissertation. Case Western Reserve University, 2017. OhioLINK Electronic Theses and Dissertations Center. 10 Dec 2017.

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Pech, Thomas "A Deep-Learning Approach to Evaluating the Navigability of Off-Road Terrain from 3-D Imaging." Electronic Thesis or Dissertation. Case Western Reserve University, 2017. https://etd.ohiolink.edu/

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