## 1 Introduction  
Automated guided vehicles (AGV) have been transporting and storing products and materials for many years in various industries and areas of activity, including in factories, warehouses, distribution centers, office buildings and hospitals [1, 2]. In order for AGVs to successfully carry out the tasks entrusted to them, it is necessary to correctly implement the two most important goals of mobile robotics, i.e., location and navigation. The location is the AGV's knowledge of where it is in the workspace. Without this knowledge, it is impossible to navigate, i.e., to determine the route, and, thus, to reach the designated point. Location and navigation processes have been important research topics for many years, and have led to the improvement of many recognized and verified techniques, as well as the development of new ones. In the case of AGVs, the most common navigation technique is odometry. The main assumption of odometry is to determine the present position of the vehicle on the basis of the distance traveled through a characteristic point of the vehicle. Although this method can provide accurate information over short periods of time, its disadvantage is the unlimited accumulation of position errors over time [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]. Odometry errors are divided into two groups: executive errors resulting from imperfections of the real object and errors resulting from the operating conditions (wheel slip, surface unevenness). To reduce the negative impact of these errors on the operation of AGVs, additional measurement methods are used. Most of the AGV tasks require a reliable location for a long time; therefore, additional sensors are needed to correct odometry errors. As measurement techniques are subject to continuous development, the number of methods used to correct odometry errors is increasing [16, 17, 18]. Among the additional measurement techniques, we distinguish two main groups of techniques that are used. The first group includes techniques in which measurements are performed with a constant frequency, e.g., laser, gyroscopic and GPS techniques. The second group consists of techniques in which the measurement is performed after the vehicle approaches a specific point, line or reference surface. Examples of such techniques are magnetic, vision, laser and ultrasonic methods [19; 20]. After the measurement is made, the vehicle course is corrected. One of the most frequently used measurement techniques supporting odometry is the technique using laser rangefinders. Laser rangefinders can be used in mobile robotics in a number of different ways. Their properties, such as their good precision and measurement accuracy, make them very effective in range measurements, so they can be used in any task that is solved with mobile robotics. Using a laser rangefinder, an AGV vehicle can create a map of an unknown environment, extracting its characteristic features (e.g., walls, corners), and in the next stage, it can locate itself on the map by measuring the distance to these objects [21; 22; 23; 24]. Thanks to this method, it is possible to perform absolute measurements of the position and orientation of the vehicle on the map, which can be used to eliminate odometry errors [7; 25]. In other methods used for the location of AGVs, laser rangefinders are employed, which use artificial orientation points installed in specific places in the working space of the AGV [26]. Measurements from laser rangefinders are also used in methods for optimizing the robot path planning process [27]. They are also applicable to new measurement techniques using neural networks. An example is [28], the authors of which proposed an algorithm for laser detection of people and for avoiding obstacles for a mobile robot that is used to transport materials inside hospitals. The authors used the deep learning method along with a neural network for the effective classification of each laser scanning point. Another example of the use of laser rangefinders is [29].