Robotics, Kalman Filter, Robot Operating System, State Estimation, Artificial Intelligence.
0.01] self.kf = KalmanFilter(mu0, Sigma0, proc_noise_std, obs_noise_std) Our simple KalmanFilter object is initialized with a prior N~(μ0, Σ0) that represents our initial belief in the state of the robot. Since we want to estimate the pose of the robot, mu0 is initialized with the vector (0.0, 0.0,