The most critical part of the programming of an autonomous mobile robot is its model creation. In a practical application it is not sufficient to design only the robot’s path, the surrounding objects have to be manipulated also, which requires an internal model of the surrounding objects. This paper presents a method for model creation. It is based on the combination of a global optimization algorithm and feature detection. It examines how different type of data can be build into the optimization process. The paper evaluates the algorithm in a two-dimensional simulated environment. It shows how to identify predefined objects using a single grayscale image.