def setup(): sim = PlaybackSensor("data/vehicle_state.json", ["fStwAng", "fVx", "fYawrate"]) # set up kalman filter tracker = BicycleEKF_noVel(dt) tracker.R = sim_var + measurement_var tracker.x = np.array([[0, 0]]).T tracker.P = np.eye(2) * 500 return sim, tracker
def setup(): sim = PlaybackSensor("data/vehicle_state_integrated.json", ["fVx", "fStwAng", "pose_x", "pose_y"]) # set up kalman filter tracker = SimpleBicycleEKF(dt, wheelbase, var_vel, var_steer) tracker.Q = np.eye(2) * 0.001 tracker.H = np.array([[1, 0, 0], [0, 1, 0]]) tracker.R = var_measurement tracker.x = np.array([[0, 0, 0]]).T tracker.P = np.eye(3) * 500 return sim, tracker
def setup(): sim = PlaybackSensor("data/vehicle_state_adenauer.json", fields=["fYawrate", "fVx"], control_fields=["fStwAng", "fAx"]) # set up kalman filter tracker = BicycleUKF(dt) Q_factor = np.array(1e-5) tracker.Q = np.diag([1, 1, 1] * Q_factor) tracker.R = R_proto * (sim_var + measurement_var) * 1 tracker.x = np.array([0, 0, 1]).T tracker.P = np.eye(3) * 1e0 return sim, tracker
def setup(): sim = PlaybackSensor("data/vehicle_state.json", fields=["fYawrate", "fVx"], control_fields=["fStwAng", "fAx"]) # set up kalman filter tracker = BicycleUKF(dt) Q_factor = np.array(5e-7) tracker.Q = np.diag([1, 1, 0.5] * Q_factor) tracker.R = R_proto * (sim_var) * filter_misestimation_factor tracker.x = np.array([0, 0, 1]).T tracker.P = np.eye(3) * 1e0 return sim, tracker
def setup(): sim = PlaybackSensor("data/vehicle_state.json", fields=["fYawrate", "fVx"], control_fields=["fStwAng", "fAx"]) # set up kalman filter tracker = BicycleEKF(dt) tracker.R = sim_var + measurement_var tracker.x = np.array([[0, 0, 1e-3]]).T tracker.P = np.eye(3) * 500 tracker.var_steer = var_steer tracker.var_acc = var_acc return sim, tracker
def setup(): sim = PlaybackSensor("data/vehicle_state.json", fields=["fYawrate", "fVx"], control_fields=["fStwAng", "fAx"]) # set up kalman filter tracker = BicycleEKF(dt) tracker.R = R_proto * (sim_var + measurement_var) tracker.x = np.array([[0, 0, 1e-3]]).T tracker.P = np.eye(3) * 500 # control not as accurate anymore tracker.var_steer = 0.0005 tracker.var_acc = 0.5 return sim, tracker
def setup(): sim = PlaybackSensor("data/vehicle_state.json", fields=["fYawrate", "fVx"], control_fields=["fStwAng", "fAx"]) # set up kalman filter tracker = BicycleUKFBias(dt) Q_factor = np.array(5e-7) tracker.Q = np.diag([1, 1, 1, 1e-11] * Q_factor) tracker.R = R_proto * (sim_var + measurement_var) * filter_misestimation_factor tracker.x = np.array([0, 0, 1, 0.35 * np.pi / 180]).T tracker.P = np.eye(4) * 1e0 tracker.P[3, 3] = 1e-8 return sim, tracker
def setup(): F = generate_F_matrix(velocity=0.001) H = np.array([[0, 1]]) sim = PlaybackSensor("data/vehicle_state.json", ["fVx", "fYawrate", "fStwAng"]) # set up kalman filter tracker = KalmanFilter(dim_x=2, dim_z=1) tracker.F = F tracker.Q = np.eye(2) * 0.001 tracker.H = H tracker.R = measurement_var tracker.x = np.array([[0, 0]]).T tracker.P = np.eye(2) * 500 return sim, tracker
def setup(): sim = PlaybackSensor("data/vehicle_state.json", fields=["fYawrate", "fVx"], control_fields=["fStwAng", "fAx"]) # set up kalman filter tracker = BicycleEKF(dt) tracker.R = R_proto * (sim_var + measurement_var) * \ filter_misestimation_factor tracker.x = np.array([[0, 0, 1e-3]]).T tracker.P = np.eye(3) * 500 # control not as accurate anymore tracker.var_steer = 0 tracker.var_acc = 0 Q_factor = np.array(5e-7) tracker.Q = np.diag([1, 1, 0.5] * Q_factor) return sim, tracker