SLAM

Simultaneous Localization and Mapping (SLAM) is a computational problem in robotics and computer vision that involves constructing a map of an unknown environment while simultaneously keeping track of an agent's location within that environment. The SLAM process typically employs probabilistic methods, such as the Extended Kalman Filter (EKF) or particle filters, to estimate the state of the robot and the map. The mathematical formulation often involves state estimation represented as a joint probability distribution over the robot's pose and the map features. SLAM algorithms can be categorized into two main types: filter-based approaches, which update the map and localization estimates sequentially, and optimization-based approaches, which refine the estimates by minimizing the error in the observed data. SLAM is essential for autonomous navigation, enabling robots and vehicles to operate in real-world environments without prior knowledge of their surroundings.