Whatever we do, wherever we go, most of the time we know “where” we are—in Berlin, at home, in front of the fridge, etc. For a successful interaction with the environment (e.g., switching on the TV set) the knowledge of one's own position relative to the object is essential. Thus, an artificial being, like a robot, which should perform similar to a human also has to know its environment and especially its position in it. In this talk we introduce some mathematical groundings for Bayesian modeling and discuss, in particular, the Monte-Carlo particle filter which can be used to model the position of a robot in a dynamic world. We will also illustrate the gray theory with interesting videos and examples from robot soccer.