The proposed algorithm segments planar structures out of data gained from 3D laser range scanners, typically used in robotics. The approach first fits planar patches to the dataset, using a new, extended Expectation Maximization (EM) algorithm. This algorithm solves the classical EM problems of insufficient initialization by iteratively determining the number and positions of patches in a split and merge framework. Determining the fitting quality of the gained patches, the approach then allows for segmentation of planar surfaces out of the 3D environment. The result is a set of 2D objects, which can be used as input for classical computer vision applications, in particular for object recognition. Our approach makes it possible to apply classical tools of 2D image processing to solve problems of 3D robot mapping, e.g. landmark recognition.