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- W423686938 abstract "Appearance--based visual learning and recognition techniques thatare based on models derived from a learning set of 2D images arebeing widely used in computer vision applications. In robotics,they have received most attention in visual servoing andnavigation.In this thesis we discuss a framework for visualself--localization of mobile robots using a parametric model builtfrom panoramic snapshots of the environment. Particularly, wepropose solutions to the problems related to robustness againstocclusions, illumination and invariance to the rotation of thesensor. Our principal contribution is the ``eigenspace ofspinning--images'', i.e., a model of environment whichsuccessfully exploits some of the specific properties of panoramicimages in order to efficiently calculate the optimal subspace interms of Principal Components Analysis (PCA) of a set of trainingsnapshots without actually decomposing the covariance matrix.By integrating a robust recover--and--select algorithm for thecomputation of image parameters we achieve reliability even in thecase when the input images are partly occluded or noisy. Further,by applying a bank of gradient filters, we achieve a significantlevel of insensitivity to changes in the illumination of theenvironment. In this way, the robot is capable of localizingitself in realistic environments." @default.
- W423686938 created "2016-06-24" @default.
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- W423686938 date "2002-04-01" @default.
- W423686938 modified "2023-09-27" @default.
- W423686938 title "Eigenspaces of Panoramic Images for Mobile Robot Localization" @default.
- W423686938 hasPublicationYear "2002" @default.
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