Autonomous Ground Vehicles designed for extreme environments (e.g mining, constructions, defense, exploration applications) require a reliable estimation of terrain traversability, in terms of both terrain slope and obstacles presence. In this paper we present a new technique to build, in real time and only from a 3D points cloud, a dense terrain elevation map able to: 1) provide slope estimation; 2) provide a reference for segmenting points into terrain's inliers and outliers, to be then used for obstacles detection. The points cloud is first smartly sampled into a 2.5 grid map, then samples are fitted into a rational B-Spline surface by means of re-weighted least square fitting and equalization. To meet an extensive range of extreme off-road scenarios, no assumptions on vehicle pose are made and no road infrastructure or a-priori knowledge about terrain appearance and shape is required. The algorithm runs in real time; it has been tested on one of VisLab's AGVs using a modified SGM-based stereo system as 3D data source.
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