SLAM refers to Simultaneous Localization and Mapping and is one of the most common problems in robot navigation. Since a mobile robot does not have hardcoded information about the environment around itself, it uses sensors onboard to construct a representation of the region. The robot tries to estimate its position with respect to objects around it like trees, building, and so on. This is, therefore, a chicken-egg problem, where the robot first tries to localize itself using objects around it and then uses its obtained location to map objects around it; hence the term Simultaneous Localization and Mapping. There are several methods for solving the SLAM problem. In this section, we will discuss special types of SLAM using a single RGB camera.
Visual SLAM methods extend visual odometry by computing a more robust camera trajectory as well as constructing a robust representation...