This chapter proposes the mapping of internal and/or external environments through a system of stereo cameras of low-cost with metric representation of the environmental in a probabilistic occupancy grid. For these reasons, cameras are playing an important role among the sensors used for robotic mapping. Cameras are sensors that, at each day, are getting cheaper and they make possible the acquisition of a large amount of information surrounding the robot. Lasers are highly accurate and provide the acquisition of detailed maps but they are not attractive because of its high cost. However, sonar presents significant inaccuracies in the measurements acquired. Besides being relatively inexpensive they can be easily found in a commercial market. The sonar is attractive because of its low cost. The most common are sonar, lasers and cameras. Several types of sensors can be used for carrying out the mapping. External terrain mapping depends on the objects that can be highlighted in them (transit cards, buildings, trees, and rocks, among others). It is well known that internal environments are more structured, so that the vast majority of them have common cues, for example, lines, nooks and corners. The difficulty is in the detection of characteristics inherent in all kind of environments. However, the majority of the works dealing with the robotics mapping theme do not discuss a generalist alternative to provide the mapping of both internal and external environments. The map built by the vehicle was composed of typical objects of the environment, as trees and stakes. has implemented a mobile vehicle to map an environment typical of a farm. Dealing with external environments, Guivant et. The robot finds points, lines and circles in the environment through processing of the information provided by sonar. , for example, uses a mobile robot equipped with sonar to build a features-based map. Robots can be used to map internal or external environments depending on the task type supported by them. Within the metric representations, the occupancy grid stands out by providing a relatively accurate reproduction of the mapped environment. The metric representations store geometric properties of the environment, whereas the topological representations describe connectivity between different places. Thrun proposes a classification of the ways of representing an environment mapped into two main categories: metric and topological representations. provides a good overview on articles published at the very early period of research about mapping and it reports the several different ways of representing the environment, focusing on the main features of each approach. There are several researches done in robotics mapping proposing ways to represent a mapped environment, all of them concerned in dealing with high dimensional mapped environment. Therefore there is a mutual dependence between inferring the exact localization of the robot and building an accurate map. 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. 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. The robot can safely navigate, identify surrounding objects and have flexibility to dealing with unexpected situations. The environment map allows mobile robots to interact coherently with objects and people in this environment. The robotic mapping can be defined as the process of acquiring a spatial model of the environment through sensory information. Environment mapping is considered an essential skill for a mobile robot in order to actually reach autonomy.