Landmark selection for terrain matching

Abstract

We describe techniques to optimally select landmarks in order to perform mobile robot localization by matching terrain maps. The method is based upon a maximum-likelihood robot localization algorithm that efficiently searches the space of possible robot positions. We use a sensor error model to estimate the probability distribution of the terrain expected to be seen from the current robot position. The estimated distribution is compared to a previously generated map of the terrain and the optimal landmark is selected by minimizing the predicted uncertainty in the localization. This approach can be used to generate a sensor uncertainty field for use by the robot's planning component. Experiments indicate that landmark selection improves not only the localization uncertainty, but also the likelihood of success.