# Positioning Systems for Unmanned Underwater Vehicles: A Comprehensive Review
## 6 Discussion
Using the data from Table 1, we developed Table 2, where each reference was categorized based on technology, range, overall cost, maintenance cost, and suitability for various depths. As noted in paper [15], the synthetic positioning system with LiDAR technology can improve camera performance and operation with a high degree of accuracy in deep-water environments. From Table 1, we can extract insights from sources [15; 44; 100; 101; 102] regarding the time-consuming and high-cost maintenance required for recalibration. As highlighted in Sections 3.4 and 4, maintenance costs are a crucial factor to consider. Our research indicates that very few papers have addressed the maintenance costs necessary for the successful operation of these specific positioning systems. In Table 2, we analyzed 27 papers, of which 22 did not mention maintenance costs at all. However, papers [15; 32; 38; 103; 110] either discussed maintenance costs or emphasized that they are significant. Despite the publication dates of these papers, it is essential that both conference papers and articles explicitly address maintenance costs when developing or acquiring positioning systems or UUVs in general. The oldest paper we reviewed was [103], published in 1979, yet it provided information that many more recent papers lacked. For instance, [16], a review paper, offered minimal details on underwater gliders. According to Table 2, the most informative paper was [105], which provided extensive information on range, with specific figures, as well as depth suitability, overall cost, and especially maintenance costs. In contrast, papers [69; 73; 104] offered limited details regarding range, overall cost, maintenance cost, and depth suitability. While [109] did not provide an exact figure for the maintenance cost of LiDAR systems, it provided a comparative understanding of what those costs could be relative to other expensive methods. The authors also noted that LiDAR can be a cost-effective solution when compared to the combination of INS, LBL, and DVL. Though combined positioning methods may offer higher accuracy, they can significantly increase overall costs. In [15], we see how INS combined with LiDAR can help in stabilization for better camera quality and a more precise mapping experience. Although [69] mathematically explains how photogrammetry works and how we can improve positioning with it, it provides little info on applications or other key factors we need, as showcased in Tables 2 and 3. Several papers, including [21; 39; 49; 79; 93; 110], propose or utilize combinations of positioning systems to enhance localization, navigation, and stabilization. Of the 27 papers reviewed, 12 use INSs either to complement other technologies or as their primary positioning system. Six of these papers [21; 39; 52; 53; 86; 110] incorporate DVL, with five of them [21; 39; 52; 53; 110] combining INS and DVL. Paper [32] focuses on IMUs, which are critical components of INSs. While [87] explores underwater navigation, it presents interesting research on using biomimetic methods to improve navigation accuracy and positioning systems. Finally, [20] acknowledges the difficulties of navigation and positioning in GPS-denied environments like underwater settings, noting that subsea navigation methods can improve mission accuracy. However, it does not specify which navigation system was employed in their project. Table 4 presents a wide array of references that examine the accuracy of underwater navigation and positioning systems, showcasing a mixture of quantitative and qualitative data. This comparison highlights both the strengths and gaps in the current body of literature, with some studies providing clear, detailed accuracy metrics while others focus on qualitative factors without numerical precision. By analyzing these studies together, we can identify trends in the literature and pinpoint areas that require further investigation. Several references provide detailed and precise accuracy data, making them essential for understanding system performance.