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<p>Computer-based vision is becoming a primary sensor mechanism in many  facets of real world 2-D and 3-D applications, including autonomous robotics,  augmented reality, object recognition, motion tracking, and biometrics. Vision's  ability to utilize non-volatile features to serve as permanent landmarks in motion  tracking provides a superior basis for applications such as initial self-localization,  future re-localization, and 3-D scene reconstruction and mapping. Furthermore,  the increased reliance of the United States armed forces on the standoff warfighting  capabilities of unmanned and autonomous vehicles (UXV) in, on, and  above the sea, necessitates better overall navigation capabilities of these  platforms. Towards this end, we draw upon existing technology to measure and  compare current visual interest point extractor performance. We utilize an  inventory of extractors to define and track interest points through physical  transformations captured in images of various scene classifications. We then  perform a preliminary determination of the best-suited extraction descriptor for  each visual scene given multi-frame interest point persistence with maximum  viewpoint invariance. Our research contributes an important cornerstone  towards the validation of precision, vision-based navigation, thereby increasing  UXV performance and strengthening the security of the United States and her  allies worldwide.
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<br>Subjects: Vehicles, Remotely piloted; Mobile robots; Computer vision; Robot vision; Pattern recognition systems</div>
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McKeehan, Zachary D.</div>
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