The chapter gives an overview on selected issues of the world modeling for autonomous systems. The introduction describes the notion of "autonomy", introduces a terminology of the subject and gives an example workflow of a common autonomous intelligent system. The following section examines domains of the world modeling, specifying the information flow and possible concepts. Then the information fusion mechanisms are presented starting from the information representation topic and a common Bayesian framework to the state of the art methods as Kalman filter and its extensions. Next, the world modeling architecture is described in details, giving information about dynamic modeling, prior knowledge, progressive mapping and entropy-based evaluation of the model. The following section introduces the idea of concept learning for dynamic extension of the prior knowledge. Finally, the experimental set-ups are described on example of three projects, followed by conclusion and references.