Current Search: Department of Civil, Environmental and Geomatics Engineering (x) » Blankenship, Jason R. (x)
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Title
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Using Deep Learning Semantic Segmentation to Estimate Visual Odometry.
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Creator
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Blankenship, Jason R., Su, Hongbo, Florida Atlantic University, College of Engineering and Computer Science, Department of Civil, Environmental and Geomatics Engineering
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Abstract/Description
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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,...
Show moreIn 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.
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Date Issued
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2018
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PURL
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http://purl.flvc.org/fau/fd/FA00005990
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Subject Headings
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Image segmentation, Computer vision, Deep learning, Visual odometry
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Format
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Document (PDF)
