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In this paper we present an information-based approach to solve the SLAM problem using stereo vision. This approach results for an improvement, in terms of both efficiency and robustness, of our early multi-view ICP randomized algorithm. Instead of minimizing an ICP-based cost, we propose the minimization of the entropy of the 2D distribution induced by the projection of the 3D point cloud. In addition we embed both the egomotion/action estimation algorithm which precedes global rectification and the new global rectification algorithm in an autonomous exploration schema. We assume plane-parallel environments and, for the sake of efficiency, we also assume a flat floor and a fixed stereo camera mounted on the robot. We show successful experiments both under tele-operating the robot and under autonomous navigation.