Localization and mapping are fundamental problems in mobile robotics. On the one hand, it is crucial to know where the robot is located in its environment in order to perform high-level applications such as delivery tasks. On the other hand, maps of the environment are often not available at the outset. Therefore, the ability to build a map of the environment is often an essential requirement. In the literature, the localization problem has been intensively studied in the past. It can be divided into two different classes. In the first class, robot localization is solved under the assumption that a map of the environment is known. It is the goal to track the robot’s position in a map. If the start location is unknown, we refer to it as global localization. In the second class, the environment is unknown so it is required to build a map on the fly. This problem is called simultaneous localization and mapping (SLAM). In general, SLAM is a harder problem than localization in known environments since mapping and localization needs to be solved at the same time. SLAM is often considered to be a chicken-and-egg problem: A map is necessary to localize a robot, and the robot’s position and orientation should be known in order to build a map. Thus, we have got circular cause and .

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