## 2 Background  
In our study, we considered the Arctic region as a great use case that poses both challenges of input DQ degradation and GPS signal instability. This region experiences a long winter, lasting up to 10 months, which creates severe problems for AV operation. The air temperature can drop to \(-60\) degrees Celsius, and the strong cold winds can reach up to 30 m/s. The increased frequency of snowfall and blizzards, as well as the small possibility of manual search and rescue in the case of emergency situations, exacerbate the challenges. Such climatic conditions significantly affect AV technical component operation. The excess resource consumption, fast battery discharge, and failures of electronic devices that are not adapted for low temperatures are some of the issues faced. The remote and inaccessible nature can limit the GPS signal's availability, thus making navigation challenging for AVs. The extreme weather conditions, low visibility, and harsh terrain can further compound the challenge of GPS signal loss [22], thus potentially leaving AVs stranded and unable to navigate safely. To address the challenges of AV navigation in such arduous areas as the Arctic region, AVs may use alternative navigation and localization systems in addition to GPS, e.g., IMU that relies on inertial sensors to track changes in position and movement direction or a computer-vision-based system, which employs on-board cameras to identify and track the position based on visual landmarks. These systems can complement GPS-based localization, thereby providing redundancy and ensuring that AVs can continue to navigate safely and accurately, even in the event of GPS signal loss. Some developments have been introduced to realize the AV's computer-vision-based localization system. [PERSON] et al. [23] presented an approach to improve the mobile robot navigation via extracting the information from the topological map. The approach was designed mainly for outdoor areas; however, it requires a sufficient number of visual reference points to navigate the robot effectively. Data-driven ML methods are also widely used to approach AV navigation [24; 25]. Here, learning approaches relying on expert reference [26] or based on reinforcement learning [27] may be employed. These learning approaches make it possible to improve AV navigation performance when the navigation experience is increasing over the operation time. This might happen in the conditions of a dynamic environment, which is typical for operating in the real world. However, ML-based navigation approaches may poorly generalize and easily \"forget\" previous knowledge that is displaced by recent experience. This inevitably leads to AV overall navigation performance degradation. The analysis of existing AV computer-vision-based navigation and localization approaches demonstrates that these methods usually require a substantial number of visual landmarks in order to maintain the level of navigation performance that is acceptable by the user and application requirements. Most of the presented approaches are primarily acceptable for indoor navigation, as its environment can often be characterized by a large number of informative reference points. Outdoor navigation is much more challenging, as its environment usually provides insufficient visual information for accurate positioning and localization, especially in specific areas such as the Arctic region. In these conditions, computer-vision-based navigation and localization methods might not be effective. To approach this challenge, additional artificial visual landmarks may be introduced to the AV's operating environment for more accurate localization. The AprilTag is one of the known examples of such artificial landmarks [21]. These tags may carry various information that might be used by AVs to perform localization and mapping on the terrain; for example, they might provide the current GPS coordinates where these tags are allocated.