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b/sensor_fusion/1D_fusion_comparison.py index 679e216..53266d0 100644 --- a/sensor_fusion/1D_fusion_comparison.py +++ b/sensor_fusion/1D_fusion_comparison.py @@ -14,7 +14,7 @@ def __init__(self, init_state, control_dt, sensors=[]) -> None: self.A = np.array([[1, self.dt], [0, 1]]) self.B = np.array([[0], [self.dt]]) - self.motion_q = np.diag([0.2, 0.05]) + self.motion_q = np.diag([0.5, 0.05]) def move(self, acceleration): dead_state = self.A.dot(self.dead_reckon_states[-1]) + self.B.dot(acceleration) diff --git a/sensor_fusion/2D_fusion.py b/sensor_fusion/2D_fusion.py index f87940d..54b6e09 100644 --- a/sensor_fusion/2D_fusion.py +++ b/sensor_fusion/2D_fusion.py @@ -54,18 +54,19 @@ def get_estimated_covariances(self): return np.array(covariances) -simulation_time = 20 # seconds -f_control = 20 # seconds -f_imu = 10 # seconds -f_gps = 0.5 # seconds +simulation_time = 6 # seconds +f_control = 10 # seconds +f_imu = 5 # seconds +f_gps = 1 # seconds def test_full_fusion(): - Q = np.diag([0.2**2, 0.2**2, 0.05**2]) + Q = np.diag([0.1, 0.1, 0.01]) + # Q = np.diag([0.2**2, 0.2**2, 0.05**2]) # np.random.seed(10) init_state = np.array([0,0,-np.pi/4]) - gps = GPS(np.array([[1, 1, 0]]), np.diag([0.1**2]), f_gps) - mag = Magnotometer(np.array([[0, 0, 1]]), np.diag([0.3**2]), f_imu) + gps = GPS(np.array([[1, 1, 0]]), np.diag([0.01**2]), f_gps) + mag = Magnotometer(np.array([[0, 0, 1]]), np.diag([0.8]), f_imu) car = MultiSensorRobot(init_state, Q, 1/f_control, [gps, mag]) init_belief = Gaussian(init_state, np.diag([1**2, 1**2, 0.5**2])) @@ -133,6 +134,7 @@ def plot_2D_comparison(filter_list, car, label): a1.set_ylabel("Position") a2.set_ylabel("Heading") a3.set_ylabel("Position Covariance") + # a3.set_yscale("log") a4.set_ylabel("Heading Covariance") true_states = np.array(car.true_states) a1.plot(true_states[:, 0], true_states[:, 1], label="True Position", color='k') @@ -140,7 +142,7 @@ def plot_2D_comparison(filter_list, car, label): a1.legend() a3.legend() - plt.savefig(f"media/1D_car_{label}.svg") + plt.savefig(f"media/2D_car_{label}.svg") test_full_fusion() diff --git a/sensor_fusion/2D_fusion_video.py b/sensor_fusion/2D_fusion_video.py new file mode 100644 index 0000000..6476fd7 --- /dev/null +++ b/sensor_fusion/2D_fusion_video.py @@ -0,0 +1,212 @@ +import numpy as np +import matplotlib.pyplot as plt +from sensor_fusion.utils.Gaussian import Gaussian +from sensor_fusion.robots.MultiSensorRover import MultiSensorRobot, GPS, IMU, Magnotometer +from matplotlib.animation import FuncAnimation + + +class SensorFusionEKF: + def __init__(self, init_belief, f, F_k, Q): + self.beliefs = [init_belief] + self.prior = None + self.f = f + self.F_k = F_k + self.Q = Q + + self.sensors = [] + self.sensor_index = {} + + def add_sensor(self, sensor): + self.sensors.append(sensor) + self.sensor_index[sensor.name] = len(self.sensors)-1 + + def control_update(self, u): + mean = self.beliefs[-1].get_mean() + cov = self.beliefs[-1].get_covariance() + mean_ = self.f(mean, u) + F = self.F_k(mean, u) + cov_ = F.dot(cov).dot(F.T) + self.Q + prior = Gaussian(mean_, cov_) + self.beliefs.append(prior) + + def measurement_update(self, y): + for m in y.keys(): + if y[m] is None: continue + if not m in self.sensor_index.keys(): continue + idx = self.sensor_index[m] + C = self.sensors[idx].C + R = self.sensors[idx].R + measurement = y[m] + + mean = self.beliefs[-1].get_mean() + cov = self.beliefs[-1].get_covariance() + L = cov.dot(C.T).dot(np.linalg.inv(C.dot(cov).dot(C.T)+R)) + mean_ = mean + L.dot(measurement-C.dot(mean)) + cov_ = (np.eye(mean.size)-L.dot(C)).dot(cov) + self.beliefs[-1] = Gaussian(mean_, cov_) + + def get_estimated_states(self): + states = [belief.get_mean() for belief in self.beliefs] + return np.array(states) + + def get_estimated_covariances(self): + covariances = [belief.get_covariance() for belief in self.beliefs] + return np.array(covariances) + + +simulation_time = 7 # seconds +f_control = 10 # seconds +f_imu = 5 # seconds +f_gps = 1 # seconds + + +def test_full_fusion(): + np.random.seed(30) + Q = np.diag([0.01, 0.01, 0.02]) + # Q = np.diag([0.2**2, 0.2**2, 0.05**2]) + # np.random.seed(10) + init_state = np.array([22,22,-np.pi/6]) + gps = GPS(np.array([[1, 1, 0]]), np.diag([0.01**2]), f_gps) + mag = Magnotometer(np.array([[0, 0, 1]]), np.diag([1]), f_imu) + car = MultiSensorRobot(init_state, Q, 1/f_control, [gps, mag]) + init_belief = Gaussian(init_state, np.diag([1**2, 1**2, 0.5**2])) + + ekf_no_sensor = SensorFusionEKF(init_belief, car.f, car.F_k, Q) + ekf_mag_only = SensorFusionEKF(init_belief, car.f, car.F_k, Q) + ekf_mag_only.add_sensor(mag) + ekf_gps_only = SensorFusionEKF(init_belief, car.f, car.F_k, Q) + ekf_gps_only.add_sensor(gps) + ekf_full_fusion = SensorFusionEKF(init_belief, car.f, car.F_k, Q) + ekf_full_fusion.add_sensor(mag) + ekf_full_fusion.add_sensor(gps) + + controls = np.sin(np.linspace(0, simulation_time, simulation_time*f_control) *1) *0.1 + # controls = np.sin(np.linspace(0, simulation_time, simulation_time*f_control) *1) *0.1 + controls = -0.1 * np.ones(simulation_time * f_control) + + for t in range(simulation_time * f_control): + car.move(controls[t]) + ekf_no_sensor.control_update(controls[t]) + ekf_mag_only.control_update(controls[t]) + ekf_gps_only.control_update(controls[t]) + ekf_full_fusion.control_update(controls[t]) + + measurement = car.measure() + ekf_no_sensor.measurement_update(measurement) + ekf_mag_only.measurement_update(measurement) + ekf_gps_only.measurement_update(measurement) + ekf_full_fusion.measurement_update(measurement) + + car_states = np.array(car.true_states) + print(f"Mean Error (no sensor): {np.mean(np.abs(ekf_no_sensor.get_estimated_states() - car_states)):.3f}") + print(f"Mean Error (mag only): {np.mean(np.abs(ekf_mag_only.get_estimated_states() - car_states)):.3f}") + print(f"Mean Error (gps only): {np.mean(np.abs(ekf_gps_only.get_estimated_states() - car_states)):.3f}") + print(f"Mean Error (full fusion): {np.mean(np.abs(ekf_full_fusion.get_estimated_states() - car_states)):.3f}") + + plot_2D_comparison([ekf_no_sensor, ekf_gps_only, ekf_mag_only, ekf_full_fusion], car, "comparison") + +from matplotlib import patches as pat + +from matplotlib.patches import Rectangle +def plot_2D_comparison(filter_list, car, label): + fig = plt.figure(figsize=(10, 5)) + a1 = plt.subplot2grid((2, 2), (0, 0), rowspan=2) + a2 = plt.subplot2grid((2, 2), (0, 1)) + a3 = plt.subplot2grid((2, 2), (1, 1)) + + labels = ["No Sensor", "GPS Only", "Magnotometer Only", "GPS + Magnotometer"] + state_list = [filter.get_estimated_states() for filter in filter_list] + cov_list = [filter.get_estimated_covariances() for filter in filter_list] + pos_cov_list = [(cov[:, 0, 0]**2 + cov[:, 1, 1]**2)**0.5 for cov in cov_list] + # belief_list = [filter.beliefs for filter in filter_list] + full_beliefs = filter_list[3].beliefs + + mean, major, minor, angle = full_beliefs[0].marginalise(2).get_confidence_ellipse(1) + arc = pat.Arc((mean[0],mean[1]),major,minor,angle, ec="#fa8231", color="#fa8231", linewidth=5) + a2.add_patch(arc) + head_cov_list = [cov[:, 2, 2]**0.5 for cov in cov_list] + true_states = np.array(car.true_states) + print(f"True: {true_states[:5]},") + + pos_lines = [] + pos_cov_lines = [] + head_cov_lines = [] + rect = Rectangle((25, 25), 3, 1, color='lightgrey') + # rect = Rectangle((25, 25), 3, 1, rotation_point='center', color='lightgrey') + # a1.add_patch(rect) + colors = ['#f7b731', '#45aaf2', '#20bf6b', '#eb3b5a'] + # colors = ['#a55eea', '#45aaf2', '#20bf6b', '#eb3b5a'] + for i in range(4): + color = colors[i] + pos_line = a1.plot(state_list[i][0, 0], state_list[i][0, 1], label=labels[i], color=color, linewidth=2) + pos_lines.append(pos_line) + # pos_cov_line = a2.plot(pos_cov_list[i], color=color) + # pos_cov_lines.append(pos_cov_line) + head_cov_line = a3.plot(cov_list[i][0, 2, 2]**0.5, color=color, linewidth=3) + head_cov_lines.append(head_cov_line) + true_pos = a1.plot(true_states[0, 0], true_states[0, 1], label="True Position", color='k', linewidth=2) + true_pos2 = a2.plot(true_states[0, 0], true_states[0, 1], color='k', linewidth=2) + full_pos2 = a2.plot(state_list[3][0, 0], state_list[3][0, 1], color='#eb3b5a', linewidth=2) + d = a1.plot(true_states[0, 0], true_states[0, 1], 'o', color='k', markersize=10)[0] + d2 = a2.plot(true_states[0, 0], true_states[0, 1], 'o', color='k', markersize=5)[0] + + def update(t): + if t == 0: return + for i in range(4): + pos_lines[i][0].set_data(state_list[i][:t, 0], state_list[i][:t, 1]) + # pos_cov_lines[i][0].set_data(range(t), pos_cov_list[i][:t]) + head_cov_lines[i][0].set_data(range(t), head_cov_list[i][:t]) + true_pos[0].set_data(true_states[:t, 0], true_states[:t, 1]) + true_pos2[0].set_data(true_states[:t, 0], true_states[:t, 1]) + full_pos2[0].set_data(state_list[3][:t, 0], state_list[3][:t, 1]) + d.set_data(true_states[t-1, 0], true_states[t-1, 1]) + d2.set_data(true_states[t-1, 0], true_states[t-1, 1]) + + mean, major, minor, angle = full_beliefs[t].marginalise(2).get_confidence_ellipse(1) + arc.set_center((mean[0], mean[1])) + arc.set_width(major) + arc.set_height(minor) + arc.set_angle(angle) + + + pos = true_states[t-1, :2] + buffer = 6 + x_min = max(pos[0] - buffer, -1) + x_max = min(pos[0] + buffer, 29) + y_min = max(pos[1] - buffer, 0) + y_max = min(pos[1] + buffer, 27) + a2.set_xlim(x_min, x_max) + a2.set_ylim(y_min, y_max) + + a1.set_aspect('equal') + a1.set_xlim(-1, 29) + a1.set_ylim(0, 27) + a2.set_aspect('equal') + a3.set_xlim(0, 70) + a3.set_ylim(0, 2) + a1.grid(True) + a2.grid(True) + a3.grid(True) + a1.set_title("Vehicle position") + a2.set_ylabel("Position \nCovariance") + a3.set_ylabel("Heading \nCovariance") + a3.set_xlabel("Time") + a3.set_xticklabels([f"{int(i/10)}" for i in a3.get_xticks()]) + # a2.set_xticklabels([f"{int(i/10)}" for i in a2.get_xticks()]) + a1.yaxis.set_major_locator(plt.MultipleLocator(5)) + fig.legend(loc="center", bbox_to_anchor=(0.25, 0.05), ncol=3) + + # plt.savefig(f"media/2D_car_{label}.svg") + plt.tight_layout() + + # anim = FuncAnimation(fig, update, frames = 2, interval = 20) + # anim = FuncAnimation(fig, update, frames = f_control*3, interval = 20) + anim = FuncAnimation(fig, update, frames = simulation_time*f_control, interval = 20) + + anim.save('media/SensorFusion_2D.gif', writer = 'ffmpeg', fps = 7) + + + + +test_full_fusion() + diff --git a/sensor_fusion/robots/MultiSensorRover.py b/sensor_fusion/robots/MultiSensorRover.py index 8001922..67d66af 100644 --- a/sensor_fusion/robots/MultiSensorRover.py +++ b/sensor_fusion/robots/MultiSensorRover.py @@ -69,7 +69,7 @@ def __init__(self, C, R, frequency) -> None: self.dt = 1/frequency def measure(self, state, t): - if t%self.dt < 0.05: + if t%self.dt < 0.1: measurement = self.C.dot(state) + np.random.multivariate_normal(np.zeros(self.R.shape[0]), self.R) else: measurement = None return measurement

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