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Deep Learning Approach for Vision Navigation in Flight

McNally, Branden Timothy
http://rave.ohiolink.edu/etdc/view?acc_num=dayton1543397742648137

Abstract Details

2018, Master of Science (M.S.), University of Dayton, Electrical and Computer Engineering.
The recent advancements in the field of Deep Learning have fostered solutions to many complex image based problems such as image classification, object detection, and image captioning. The goal of this work is to apply Deep Learning techniques to the problem of image based navigation in a flight environment. In the situation GPS is not available, it is important to have alternate navigation systems. An image based navigation system is potentially a cost effective alternative during a GPS outage. The current state of the art results are obtained using a perspective-n-point (PnP) approach. The downsides to the PnP approach include carrying a large database of features for matching and sparse availability of distinct features in all scenes. A deep learning approach allows for a lightweight solution and provides a position estimation for any scene. A variety of published networks are modified for regression and trained to estimate a virtual drones North and East position as a function of a single input image. The best network tested produces an average euclidean distance error, in a 2.5 x 2.5 Km virtual environment, is 5.643 meters.
Eric Balster, Ph.D. (Advisor)
44 p.
Engineering
Deep Learning; Alternative Navigation; Vision Navigation; Image Based Navigation; Flight Navigation; Convolutional Neural Networks

Recommended Citations

Citations

  • McNally, B. T. (2018). Deep Learning Approach for Vision Navigation in Flight [Master's thesis, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1543397742648137

    APA Style (7th edition)

  • McNally, Branden. Deep Learning Approach for Vision Navigation in Flight. 2018. University of Dayton, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=dayton1543397742648137.

    MLA Style (8th edition)

  • McNally, Branden. "Deep Learning Approach for Vision Navigation in Flight." Master's thesis, University of Dayton, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1543397742648137

    Chicago Manual of Style (17th edition)

dayton1543397742648137
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© 2018, all rights reserved.
This open access ETD is published by University of Dayton and OhioLINK.