Accuracy bounds and optimal computation of robot localization

We present an optimal method for estimating the current location of a mobil e robot by matching an image of the scene taken by the robot with a model o f the known environment. We first derive a theoretical accuracy bound and t hen give a computational scheme that can attain that bound, which can be vi ewed as describing the probability distribution of the current location. Us ing real images, we demonstrate that our method is superior to the naive le ast-squares method. We also confirm the theoretical predictions of our theo ry by applying the bootstrap procedure.