Type of Document Dissertation Author Gong, Xiaojin Author's Email Address firstname.lastname@example.org URN etd-12192008-113007 Title Omnidirectional Vision for an Autonomous Surface Vehicle Degree PhD Department Electrical and Computer Engineering Advisory Committee
Advisor Name Title Wyatt, Christopher L. Committee Chair Abbott, A. Lynn Committee Co-Chair Baumann, William T. Committee Member Ramakrishnan, Naren Committee Member Stilwell, Daniel J. Committee Member Keywords
- Omnidirectional vision
- range estimation
- outlier rejection
- motion detection
- autonomous navigation
Date of Defense 2008-12-05 Availability unrestricted AbstractDue to the wide field of view, omnidirectional cameras have been extensively used in many applications, including surveillance and autonomous navigation. In order to implement a fully autonomous system, one of the essential problems is construction of an accurate, dynamic environment model. In Computer Vision this
is called structure from stereo or motion (SFSM). The work in this dissertation addresses omnidirectional vision based SFSM for the navigation of an autonomous surface vehicle (ASV), and implements a vision system capable of locating stationary obstacles and detecting moving objects in real time.
The environments where the ASV navigates are complex and fully of noise, system performance hence is a primary concern. In this dissertation, we thoroughly investigate the performance of range estimation for our omnidirectional vision system, regarding to different omnidirectional stereo configurations and considering
kinds of noise, for instance, disturbances in calibration, stereo configuration, and
image processing. The result of performance analysis is very important for our
applications, which not only impacts the ASV’s navigation, also guides the development of our omnidirectional stereo vision system.
Another big challenge is to deal with noisy image data attained from riverine environments. In our vision system, a four-step image processing procedure is designed: feature detection, feature tracking, motion detection, and outlier rejection. The choice of point-wise features and outlier rejection based method makes
motion detection and stationary obstacle detection efficient. Long run outdoor
experiments are conducted in real time and show the effectiveness of the system.
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