## 1 Introduction  
Each year, fire causes a significant number of fatalities as well as significant material losses. As a result, society now places a high importance on fire prevention and early detection, and thus, it is also the main area of study and development for many scientists and different sectors. Rapid technological advancement, particularly in the areas of robotics, embedded systems, and machine learning, is having an impact on how the field of firefighting is developing. This field is becoming more effective and safe, and consequently, is minimizing the danger for people and property, as well as firefighters. There are some existing robotic solutions for firefighting in the area of forest firefighting [1]. There is a number of robotic systems for indoor and outdoor firefighting that can be found in [2, 3, 4]. Over time, there have been several attempts to develop an autonomous robot for firefighting that uses advanced methods for navigation and mapping as well as different image-processing techniques for fire prevention and detection [5, 6, 7]. More recent work proposes using unmanned aerial vehicles (UAVs) for both indoor and outdoor firefighting [8, 9, 10]. Due to the many challenges involved, such as real-time prevention and detection, complex mapping and navigation inside narrow and often dark areas, changes of environment, obstacle avoidance, and balancing the processing power with energy consumption, none of the above-mentioned projects resulted in a commercially available solution. The work in this paper is focused on developing an autonomous robot for fire prevention, fire detection, and extinguishing (FPDE). More details about our work will be provided in the next section. Many existing techniques rely on using different sensors for fire prevention, as presented in [11; 12; 13; 14]. Due to their limitations, they are becoming obsolete, and with the recent advances in the field of image processing and machine learning, many new techniques for fire detection have emerged [15; 16; 17; 18]. Early detection of a potential fire hazard is most easily accomplished with an infrared thermal-imaging camera, as shown in [19; 20; 21]. An IR thermal camera is the only one capable of converting the highest excitation that can be registered by the sensor into a visual image accompanied by radiometric data. IR thermal-imaging cameras do not measure temperature but register radiation, which can be converted into object temperature after accounting for the emission coefficient of the object's surface and environmental parameters. Detection of anomalies in the form of hotspots is the basis of an approach that would provide useful information depending on the algorithm. The accuracy of the information depends on the accuracy of the camera, the correct choice of emissivity, and the angle of capture. These are the basic parameters that the algorithm analyzes. Cheap thermal-imaging cameras have been on the market since 2014, and the price of the single element of the sensor is continuously decreasing while at the same time the resolution is increasing, which, together with software support, leads to a wide range of prices for individual thermal-imaging cameras. Choosing the optimal solution is not easy, as the width of the optics and the need to see detail must be matched. For the development of FireBot (Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Osijek, Croatia), we analyzed the possibility of using seven thermal-imaging cameras and two pyrometers. Calibration, which served as the basis for comparison, was performed in the temperature range of 27 \({}^{\circ}\)C to 45 \({}^{\circ}\)C, since this range poses the greatest challenge for the early detection of anomalies. The deviation analysis provided useful information on the basis of which the accuracy and precision can be determined, as well as an indicator of the optimal selection based on indicators that include financial indicators and the deviation of the results.