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Environment mapping is considered an essential skill for a mobile robot in order to actually reach autonomy [1]. The robotic mapping can be defined as the process of acquiring a spa‐ tial model of the environment through sensory information. The environment map allows mobile robots to interact coherently with objects and people in this environment. The robot can safely navigate, identify surrounding objects and have flexibility to dealing with unex‐ pected situations. Without a map some important operations could be complex as the deter‐ mination of objects position in the surroundings of the robot and the definition of the path to be followed. These issues involve the importance of the mapping task be performed cor‐ rectly, since the acquisition of inaccurate maps can lead to errors in the inference of correct positioning of the robot, resulting in an imperfect implementation of these operations. Therefore there is a mutual dependence between inferring the exact localization of the robot and building an accurate map.

3D Probabilistic Occupancy Grid to Robotic Mapping with Stereo Vision