Robotics, Kalman Filter, Robot Operating System, State Estimation, Artificial Intelligence.
self.R = np.diag(self.proc_noise_std ** 2) # process noise covariance # Observation model (C) self.C = odometry_observation_model() # The observation model to use # Standard deviations for the noise in x, y, and theta (observation or sensor model noise) self.obs_noise_std = np.array(obs_noise_std)