Object localization and identification for autonomous operation of surface marine vehicles
Name
958163374-MIT.pdf
Description
Full printable version
Size
14.76 MB
Format
Adobe PDF
Checksum (MD5)
e9c7b334cb971d90e626103c80502bdf
Author(s)
Mentzelos, Konstantinos
Advisor(s)
Chryssostomos Chryssostomidis, Chathan Cooke and Joe Harbour.
Date Issued
2016
Publisher
Massachusetts Institute of Technology
Abstract
A method for autonomous navigation of surface marine vehicles is developed A camera video stream is utilized as input to achieve object localization and identification by application of state-of-the-art Machine Learning algorithms. In particular, deep Convolutional Neural Networks are first trained offline using a collection of images of possible objects to be encountered (navy ships, sail boats, power boats, buoys, bridges, etc.). The trained network applied to new images returns real-time classification predictions with more than 93% accuracy. This information, along with distance and heading relative to the objects taken from the calibrated camera, allows for the precise determination of vehicle position with respect to its surrounding environment and is used to compute safe maneuvering and path planning strategy that conforms to the established marine navigation rules. These algorithms can be used in association with existing tools, such as LiDAR and GPS, to enable a completely autonomous marine vehicle.
Description
Thesis: Nav. E., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2016.
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.
"June 2016." Cataloged from PDF version of thesis.
Includes bibliographical references (pages 99-100).
Subjects
Mechanical Engineering.
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Mechanical Engineering
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