<title>Segmentation of laser range images with respect to range and variance</title>

Segmentation is a first step towards successful tracking and object recognition in 2-D pictures. Mostly the pictures are segmented with respect to quantities as range, intensity etc. Here a method is presented for segmentation of 2-D laser range pictures with respect to both range and variance simultaneously. This is very useful since man-made objects differ from the background in the terrain by their smoothness. The approach is based on modeling horizontal scans of the terrain as piecewise constant functions. Since the environment has a complicated and irregular structure we use multiple models for modeling different segments in the laser range image. The switching between different models, i.e., ranges belonging to different segments in a horizontal scan, are modeled by a hidden Markov model. The method is of relatively low computational complexity and the maximal complexity can be controlled by the user. Real data is used for illustration of the method