The abilities to sense and model a dynamic environment are key components of intelligent systems. In this contribution, firstly a methodology is presented to make an ideal selection of the input data available. Then, an object oriented environment model is proposed which allows a continuous fusion of existing knowledge with new sensor information. All methods are based on Bayesian statistics in an objective "degree of belief" interpretation. Application areas are demonstrated by the examples of humanoid robots and autonomous vehicles.