<![CDATA[One of the most significant elements of a moving robot is mapping. The robot's capacity to identify its surroundings and translate them into a map allows it to navigate effectively from one spot to another while avoiding impediments that may arise during the navigation process. The SLAM method already has a mapping capability, so it can continuously localize the position against the map. In this experiment, 2D laser scanning data was obtained using RPlidar-A1 and then processed by the slam algorithm, namely gmapping and hector mapping to produce an occupancy grid map. The map is displayed by the RViz visualization widget, and its length is measured using the RViz measurement tools. The results of the occupancy grid map generated by the gmapping algorithm with a laser scan matcher have a lot of noise, and lose orientation. At the measured point length, the gmapping algorithm has slightly more error. The hector mapping algorithm has better performance than gmapping with a laser scan matcher on the RPLidar-A1 device.]]>
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