Omnidirectional Vision for an Autonomous Surface Vehicle

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Virginia Tech

Due 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.

calibration, Omnidirectional vision, range estimation, outlier rejection, motion detection, autonomous navigation