Robotics, Kalman Filter, Robot Operating System, State Estimation, Artificial Intelligence.
so effectively, we are adding 0 to the μ of the state. In line 3 the second parameter of the state is updated to complete the prediction. Σ^bar_t is obtained by projecting Σ_t-1 forward in time, following the rules of linear transformations of Gaussians. Then the matrix R_t, which is the covariance