One of the essential tasks required from a mobile robot is the autonomous and safe navigation of its working environment. However, in many cases, a model of the environment or map is not available to execute this task. Indeed, navigation requires a permanent estimation of the location for a map, which is not available for unknown environments. In such a scenario, the robot must have extended capabilities to solve, concurrently, the problems of localization and mapping. The simultaneous solution of these two problems is known as SLAM (simultaneous localization and mapping) and is a complex problem, not yet fully solved by the scientific community. This is due to the fact that localization requires a map that is not yet available since it is still under construction. In turn, the elaboration of a map requires the estimation of the robot’s location. This is the reason why SLAM has been categorized as similar to the chicken and egg problem. In the case of a robot facing an unknown environment, it would be something like what to solve first, localization or mapping? The answer to this question is that the robot will have to solve both problems at the same time. This article presents a s
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