Selecting Landmarks for Localization in Natural Terrain

We describe techniques to optimally select landmarks for performing 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 a probability distribution over 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 has been applied to the generation of a sensor uncertainty field that can be used to plan a robot’s movements. Experiments indicate that landmark selection improves not only the localization uncertainty, but also the likelihood of success. Examples of landmark selection are given using real and synthetic data.