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Many applications in mobile robotics, require that robots have the ability to move through environments with certain autonomy in order to achieve their tasks more efficiently. In some situations, they need to traverse unknown terrain and be able to acquire a model of the environment while at the same time, they require to know their location. In probabilistic robotics, the process of concurrently building up a map of an environment and using this map to obtain improved estimates of the location of a robot is usually referred as Simultaneous Localization and Mapping (SLAM). 
 
<center>![](https://steemitimages.com/DQmRzudLxiFGW2BgwXPNjtgARd3ox2fCw2nvErYSv1mERnn/image.png)</center>
Fuente: http://www.pirobot.org/blog/0015/

One significant aspect to take into account in the problem of learning accurate maps of the environments is the consideration of the sources of uncertainties of a mobile robot. In essence, the robot relies on its ability to extract useful information of the environment from the data returned by its sensors. Due to the limited accuracy of the sensor that the robot uses to perceive its environment, it represents the major source of uncertainties about the state of the world. Dealing with the problem of the uncertainty of a mobile robot is one of the most important issues to consider in evaluating the quality of the resulting map, thus minimizing the sources of uncertainties allows us to provide better maps.
 
<center>![](https://steemitimages.com/DQmQNoXSVEAgrwmHfy1QhxBhZhDBv1jgr1jScoCrnzkXRyX/image.png)</center>
Fuente: https://www.omicsonline.org/open-access/map-building-based-on-a-xtion-pro-live-rgbd-and-a-laser-sensors-2165-7866.1000126.php?aid=24908

Nowadays, several methods have been developed for mapping different types of environments. From unstructured, dynamic or large scale environments, until mapping of structured, static and limited size environments.
Therefore, the aspect of knowing the mapping of the environment where a robot moves has been one of the great topics developed by the robot community in recent years.

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