## 1 Introduction  
Some of the requirements that are placed on autonomous mobile robotic systems include real-world environments navigation and recognition and identification of objects of interest with which the robotic system have to interact. Computer vision allows machines to obtain a large amount of information from the environment that has a major impact on their behavior. In the surrounding environment there are often numerous different static objects (walls, columns, doors, and production machines) but also moving objects (people, cars, and handling trucks). Objects that are used to refine navigation, landmarks, can be artificial (usually added by a human) and natural (which naturally occur in the environment) [1, 2]. Robotic systems have to work in a real environment and must be able to recognize, for example, people [3], cars [4], product parameters for the purpose of quality control, or objects which are to be handled. The use of QR (Quick Response) codes (two-dimensional matrix codes) in interaction with robots can be seen in the following areas:  
* in the field of navigation as artificial landmarks-analogies of traffic signs that control the movement of the robot in a given area (no entry, driving direction, alternate route, and permitted hours of operation) or as an information board providing context specific information or instructions (such as identification of floor, room, pallets, and working place)in the area of object identification, 2D codes are often used to mark products and goods and thus their recognition will provide information about the type of goods (warehouses), the destination of the shipment (sorting lines) or control and tracking during track-and-trace. QR Codes are classified among 2D matrix codes (similar to Data Matrix codes). QR Codes (Model 1 and Model 2) are squared-shaped 2D matrices of dark and light squares--so called modules. Each module represents the binary 1 or 0. Each QR Code has fixed parts (such as Finder Patterns and Timing Patterns) that are common to each QR Code and variable parts that differ according to the data that is encoded by a QR Code. Finder Patterns, which are located in the three corners of a QR Code, are important for determining the position and rotation of the QR Code. The size of the QR code is determined by the number of modules and can vary from \(21\times 21\) modules (Version 1) to \(177\times 177\) (Version 40). Each higher version number comprises four additional modules per side. In the Figure 1 we can see a sample of version 1 QR Code. QR Code has error correction capability to restore data if the code is partially damaged. Four error correction levels are available (L-Low, M-Medium, Q-Quartile, and H-High). The error correction level determines how much of the QR Code can be corrupted to keep the data still recoverable (L-7%, M-15%, Q-25%, and H-30%) [5]. The QR Code error correction feature is implemented by adding a Reed-Solomon Code to the original data. The higher the error correction level is the less storage capacity is available for data. Each QR Code symbol version has the maximum data capacity according to the amount of data, character type and the error correction level. The data capacity ranges from 10 alphanumeric (or 17 numeric) characters for the smallest QR Code up to 1852 alphanumeric (or 3057 numeric) characters for the largest QR Code at highest error correction level [5]. QR Codes support four encoding modes--numeric, alphanumeric, Kanji and binary--to store data efficiently. QR Code was designed in 1994, in Japan for automotive industry, but currently has a much wider use. QR codes are used to mark a variety of objects (goods, posters, monuments, locations, and business cards) and allow to attach additional information to them, often in the form of a URL to a web page. QR Code is an ISO standard (ISO/IEC 18004:2015) and is freely available without license fees.