Nowadays, mobile robots are utilized across a multitude of domains, including everyday life and research and work environments. Consequently, research in the field of autonomous robots has expanded considerably over the past few decades. In order to perform a task, a mobile robot must be able to localize itself in the surrounding environment. However, the operating area of a mobile robot may often be unknown, inaccessible, or dangerous. In such cases, Simultaneous Localization and Mapping (SLAM) is a key functionality that allows mobile robots to construct a real-time map of an unknown environment, while simultaneously localizing themselves within it. Due to its significance, SLAM conducted by a single agent in an isolated setting has long been regarded as the state of the art in mobile robotics research.