3D Probabilistic Occupancy Grid to Robotic Mapping with Stereo Vision

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 spatial 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 unexpected situations. Without a map some important operations could be complex as the determination 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 correctly, 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.