eustoma

  SLAM Algorithm and Extended Kalman Filter (EKF) The EKF is a filtering algorithm commonly used for state estimation in systems where the underlying dynamics can be described by non-linear models. It combines predictions from a motion model with measurements from sensors to estimate the state of a system. The EKF assumes that the system's state and measurement models are differentiable and can be linearized around the current estimate. It is widely used in various applications, including robotics, navigation, and control, to estimate the state of a system with uncertain measurements and dynamic models. Once the Simultaneous Localization and Mapping (SLAM) algorithm has been executed to construct or update a map of the environment and estimate the robot's pose, the accuracy of the SLAM-based system can be further improved by applying the Extended Kalman Filter (EKF). After completing the SLAM process, the EKF can be employed as a post-processing step to refine the estimated rob...