Subpixel localization and uncertainty estimation using occupancy grids

We describe techniques for performing mobile robot localization using occupancy grids that allow subpixel localization and uncertainty estimation in the pixelized pose space. The techniques are based on a localization method where matching is performed between the visible landmarks at the current robot position and a previously generated map of the environment. A likelihood function over the space of possible robot positions is formulated as a function of the probability distribution for the map matching error. Subpixel localization and uncertainty estimation are performed by fitting the likelihood function with a parameterized surface. The performance of the method is analyzed using synthetic experiments and an example is given using the Rocky 7 Mars rover prototype