Simultaneous localization and mapping (SLAM) are used in a computational problem that constructs and updates the map of an unfamiliar environment and simultaneously keeps the agent track’s location in the location. It is used in computational geometry and robotics. It usually appears simple, but various algorithms are required to solve it. These algorithms solve it within a time that can be traceable for some environments. Some approximate solution approaches consist of the extended Kalman filter, GraphSLAM, particle filter, and Covariance intersection. These algorithms are applied to navigation, odometry for augmented reality and virtual reality, and robotic mapping. SLAM algorithms are used for tailoring the available resources at operational compliance. Therefore, the aim is never to achieve perfection. Self-driving cars, self-sufficient underwater vehicles, aerial vehicles that are unmanned, the latest domestic robots, and planetary rovers use published approaches.

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