Robotics, Kalman Filter, Robot Operating System, State Estimation, Artificial Intelligence.
model, or state transition probability, is represented by p(x_t | u_t, x_t-1). This model defines the new state ​x_t based on the previous state x_t-1​ and current control inputs u_t. To maintain Gaussian properties, it must be a linear function of its arguments, with added Gaussian noise ϵ_t to