Accuracy bounds and optimal computation of robot localization
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We present an optimal method for estimating the current location of a mobile robot by matching an image of the scene taken by the robot with a model of the known environment. We first derive a theoretical accuracy bound and then give a computational scheme that can attain that bound, which can be viewed as describing the probability distribution of the current location. Using real images, we demonstrate that our method is superior to the naive least-squares method. We also confirm the theoretical predictions of our theory by applying the bootstrap procedure.
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