This thesis illustrates based on a practical example how maps that result from simultaneous localization and mapping (SLAM) methods can be used for path planning and path optimization. In order to do so approaches to solve the SLAM problem are discussed and the properties of resulting maps are described. Furthermore the principles of path planning and optimization are addressed. By means of the TurtleBot as example of a mobile robot a possibility to appropriately deal with the properties of SLAM-based maps in path planning will be showed. Special attention will be paid to k-order Markov optimization, which is used to smoothen the paths.