In this research, image segmentation and visual odometry estimations in real time
are addressed, and two main contributions were made to this field. First, a new image
segmentation and classification algorithm named DilatedU-NET is introduced. This deep
learning based algorithm is able to process seven frames per-second and achieves over
84% accuracy using the Cityscapes dataset. Secondly, a new method to estimate visual
odometry is introduced. Using the KITTI benchmark dataset as a baseline, the visual
odometry error was more significant than could be accurately measured. However, the
robust framerate speed made up for this, able to process 15 frames per second. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2018. / FAU Electronic Theses and Dissertations Collection
| Identifer | oai:union.ndltd.org:fau.edu/oai:fau.digital.flvc.org:fau_40781 |
| Contributors | Blankenship, Jason R. (author), Su, Hongbo (Thesis advisor), Florida Atlantic University (Degree grantor), College of Engineering and Computer Science, Department of Civil, Environmental and Geomatics Engineering |
| Publisher | Florida Atlantic University |
| Source Sets | Florida Atlantic University |
| Language | English |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation, Text |
| Format | 57 p., application/pdf |
| Rights | Copyright © is held by the author, with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder., http://rightsstatements.org/vocab/InC/1.0/ |
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