The focus of this paper is on strategies for adapting a couple of internal representations to the actual environment of a mobile robot. From the range of approaches, two different (and in a sense orthogonal) basic ideas are discussed, regarding computational effort, stability, reliability, sensor requirements, and consistency as well as their useful applications. The first approach is an exact, geometric technique using line representations extracted from the information produced by a laser-range finder. The second discussed possibility is a qualitative, topologic mapping of the environment using neural clustering techniques. Both presented classes of environment-modelling strategies are evaluated on the basis of principal arguments and of simulations resp. tests on real robots. Experiences from the MOBOT resp. the ALICE project are discussed together with some related work.

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