Investigation of the accuracy of maps produced with simultanious mapping and algorithm

In robotic research, the most well-known technique for localization and mapping is the Simultaneous Localization and Mapping (SLAM) algorithm. The SLAM method optimizes the problems encountered in traditional methods, which are time-consuming processes and issues arising from the density of collected data. Additionally, it offers a wide range of applications, particularly in fields such as cartography, robotics, and autonomous vehicles. SLAM algorithms' mathematical and algorithmic components have made significant progress along with sensor and computer-based image processing technology advancements. However, research in the literature generally evaluates the performance of SLAM algorithms based on environmental factors and sensor capabilities. There is a lack of sufficient studies related to the internal components of the algorithms and the impacts arising from the user perspective. This study was conducted as a comprehensive analysis aiming to identify the drawbacks, optimize the positive aspects, and investigate the factors affecting the accuracy of the resulting products using the SLAM method suitable for photogrammetry. In this case, measurements and scans of 3D shapes were done indoors and outdoors using GNSS-enabled backpacks, handheld LiDAR systems, and smartphones with LiDAR sensors. The results revealed that GNSS-supported LiDAR systems provide significant advantages in outdoor environments, that overall loop closure plays a crucial role in the accuracy of the results, and that smart mobile devices are suitable for small structures. When comparing three handheld LiDAR systems, it was found that their geometric accuracies were similar regardless of point densities, but the visual appearance of the resulting products could vary. In conclusion, this study presents significant findings to enhance the effectiveness of using SLAM technology in the field of photogrammetry. The studies conducted within the scope of this thesis provide valuable data by evaluating the performance of SLAM algorithms and integrated systems, offering insights for future research.