Simultaneous Localization and Mapping (SLAM) is one of the main and well researched topics to robotics, namely, in the field of autonomous and environment aware robots. SLAM is used in many applications such as self-driving cars, and robotic indoor localization. This paper investigates the current methods and approaches used in mapping. It also studies the feasibility of designing an affordable mapping device using off the shelf components and open-source software. The paper includes a case study for designing a hand-help mapping device. Portable laser rangefinders (LIDAR) are the sensor used for creating the maps. AI aided computer vision is suggested for use. A full design is described including top-level, finite state machine, schematics, raw data logging, and an in-door mapping algorithm. Every aspect is tested using simulation and real data. The paper also tests an open-source operating system as an implementation environment. Also, the paper introduces an alternative approach for mapping, namely, autonomous mapping using a small, wheeled robot. Finally, all results are discussed and analyzed, also, possible future work and improvements are highlighted.