LiDAR is a measuring device that uses laser beams to record objects and phenomena in its environment. In this way, a large amount of spatial information is obtained in a considerably shorter time than was required with previous measurement methods. However, in order for the data obtained by laser scanners to be useful, it is necessary to have an understanding of the issues of the very way of creating, storing and processing the so-called 3D point clouds. Adding the problem of movement, as well as the need to monitor the location of the device for the entire recording time, results is a complex system that includes various measuring devices and software solutions. Mapping the environment while simultaneously preforming localizations is named SLAM, and due to its complexity the price of ready-made commercial SLAM systems is very high. The aim of this study is to power up the SLAM system in a way that uses a low-budget solidstate LiDAR, and as software support, an open type algorithm that is publicly available via the GitHub platform. The LiDAR device used is the Livox Mid-100, while the algorithm used is LOAM Livox, developed at the University of Hong Kong. The possible future use of such systems for geodetic or similar purposes of environmental mapping would certainly bring laser technology closer to a wider number of users. The use of more affordable new technologies would also have a positive impact on the modernization of the profession in general.