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A promising approach to accurate positioning of robots is ground texturebased localization. It is based on the observation that visual features ofground images enable fingerprint-like place recognition. We tackle the issue ofefficient parametrization of such methods, deriving a prediction model forlocalization performance, which requires only a small collection of sampleimages of an application area. In a first step, we examine whether the modelcan predict the effects of changing one of the most important parameters offeature-based localization methods: the number of extracted features. Weexamine two localization methods, and in both cases our evaluation shows thatthe predictions are sufficiently accurate. Since this model can be used to findsuitable values for any parameter, we then present a holistic parameteroptimization framework, which finds suitable texture-specific parameterconfigurations, using only the model to evaluate the considered parameterconfigurations.

Model-Based Parameter Optimization for Ground Texture Based Localization Methods