Added more docs, fun demo figure, minor renaming, some tests

README.rst

@@ -13,6 +13,12 @@ navlie
     :target: https://www.python.org/downloads/
     :alt: Python Version
 
+.. image:: ./docs/source/fun_figs.png 
+    :alt: Demo Figures
+    :align: center
+    :width: 100%
+
+
 An on-manifold state estimation library for robotics.
 
 The core idea behind this project is to abstract-away the state definition such that a single estimator implementation can operate on a variety of state manifolds, such as the usual vector space, and any Lie group. At the moment, algorithms and features of this package include:

docs/source/fun_figs.py

@@ -0,0 +1,165 @@
+# %%
+import navlie as nav
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+sns.set_style("whitegrid")
+
+# %% Banana distribution plot
+def banana_plot(ax = None):
+    N = 500
+    x0 = nav.lib.SE2State([0.3, 3, 4], direction="right")
+    covariance = np.diag([0.2**2, 0.05**2, 0.05**2])
+    process_model = nav.lib.BodyFrameVelocity(np.zeros(3))
+
+    dx_samples = nav.randvec(covariance, N).T
+    x0_samples = [x0.plus(dx) for dx in dx_samples]
+
+    # Monte-carlo the trajectory forward in time
+    dt = 0.1
+    T = 10
+    stamps = np.arange(0, T, dt)
+
+    if ax is None:
+        fig, ax = plt.subplots(figsize=(8, 8))
+
+    final_states = []
+    for sample in x0_samples:
+        x_traj = [sample.copy()]
+        u = nav.lib.VectorInput([0.1, 0.3, 0])
+        x = sample
+        for _ in stamps:
+            x = process_model.evaluate(x, u, dt)
+            x_traj.append(x.copy())
+
+        # plot the trajectory
+        traj_pos = np.array([x.position for x in x_traj])
+
+        # random greyscale color
+        color = np.random.uniform(0.3, 0.9)
+        ax.plot(traj_pos[:, 0], traj_pos[:, 1], color=(color, color, color),zorder=1)
+
+        # save the final state
+        final_states.append(x_traj[-1])
+
+    final_positions = np.array([x.position for x in final_states])
+    ax.scatter(final_positions[:, 0], final_positions[:, 1], color="C0", zorder=2)
+
+    # Propagate the mean with EKF 
+    kf = nav.ExtendedKalmanFilter(process_model)
+    x0_hat = nav.StateWithCovariance(x0, covariance)
+
+    x_hat_traj = [x0_hat]
+    for t in stamps:
+        u.stamp = t
+        x_hat_traj.append(kf.predict(x_hat_traj[-1], u, dt))
+
+    mean_traj = np.array([x.state.position for x in x_hat_traj])
+    ax.plot(mean_traj[:, 0], mean_traj[:, 1], color="r", zorder=3, linewidth=3)
+    ax.set_aspect("equal")
+
+# banana_plot()
+
+# %%
+def pose3d_plot(ax = None):
+    N = 500
+    x0 = nav.lib.SE3State([0.3, 3, 4, 0, 0, 0], direction="right")
+    process_model = nav.lib.BodyFrameVelocity(np.zeros(6))
+
+    dt = 0.1
+    T = 20
+    stamps = np.arange(0, T, dt)
+
+    x_traj = [x0.copy()]
+    u = nav.lib.VectorInput([0.1, 0.3, 0, 1, 0, 0])
+    x = x0.copy()
+    for _ in stamps:
+        x = process_model.evaluate(x, u, dt)
+        x_traj.append(x.copy())
+
+    fig, ax = nav.plot_poses(x_traj, ax = ax)
+
+
+# pose3d_plot()
+
+# %%
+def three_sigma_plot(axs = None):
+    dataset = nav.lib.datasets.SimulatedPoseRangingDataset()
+
+    estimates = nav.run_filter(
+        nav.ExtendedKalmanFilter(dataset.process_model),
+        dataset.get_ground_truth()[0],
+        np.diag([0.1**2, 0.1**2, 0.1**2, 0.1**2, 0.1**2, 0.1**2]),
+        dataset.get_input_data(),
+        dataset.get_measurement_data()
+        )
+
+    results = nav.GaussianResultList.from_estimates(estimates, dataset.get_ground_truth())
+
+    fig, axs = nav.plot_error(results[:, :3], axs=axs)
+    axs[2].set_xlabel("Time (s)")
+
+
+# three_sigma_plot()
+
+
+if __name__ == "__main__":
+
+    # Make one large figure which has all the plots. This will be a 1x3 grid, with the
+    # last plot itself being a three vertically stacked plots.
+
+    # The following values where chosen by trial and error
+    # top=0.975,
+    # bottom=0.097,
+    # left=0.025,
+    # right=0.992,
+    # hspace=0.2,
+    # wspace=0.117
+
+    # which will be used here:
+
+
+
+    fig = plt.figure(figsize=(20, 6))
+    gs = fig.add_gridspec(1, 3, width_ratios=[1, 1, 1])
+    ax1 = fig.add_subplot(gs[0])
+    ax2 = fig.add_subplot(gs[1], projection='3d')
+
+    # The last plot is a 3x1 grid
+    gs2 = gs[2].subgridspec(3, 1, hspace=0.1)
+    ax3 = fig.add_subplot(gs2[0])
+    ax4 = fig.add_subplot(gs2[1])
+    ax5 = fig.add_subplot(gs2[2])
+
+    # Remove tick labels for ax3 and ax4
+    ax3.set_xticklabels([])
+    ax4.set_xticklabels([])
+
+    # Remove all tick labels for ax2
+    ax2.set_xticklabels([])
+    ax2.set_yticklabels([])
+    ax2.set_zticklabels([])
+
+
+    banana_plot(ax1)
+    pose3d_plot(ax2)
+    three_sigma_plot(np.array([ax3, ax4, ax5]))
+
+    # Set spacing to the above values 
+    fig.subplots_adjust(
+        top=0.975,
+        bottom=0.097,
+        left=0.025,
+        right=0.992,
+        hspace=0.2,
+        wspace=0.117
+    )
+
+
+    # Save the figure with transparent background, next to this file
+    import os 
+    fig.savefig(os.path.join(os.path.dirname(__file__), "fun_figs.png"), transparent=True)
+
+    plt.show()
+# %%

docs/source/index.rst

@@ -15,6 +15,11 @@
 Welcome to navlie!
 ------------------
 
+.. make a row of three figures stacked side by side
+.. image:: ./fun_figs.png
+   :width: 100%
+   :align: center
+
 navlie is a state estimation package specifically designed for both traditional and Lie-group-based estimation problems! 
 
 The core idea behind this project is to use abstraction in such a way that both traditional and Lie-group-based problems fall under the exact same interface. Using this, a single estimator implementation can operate on a variety of state definitions, such as the usual vector space, and any Lie group. We allow the user to define their custom state, process model, and measurement models, after which they will have a variety of algorithms available to them, including:

examples/ex_ekf_vector.py

@@ -90,7 +90,7 @@ def main():
     import matplotlib.pyplot as plt
     import seaborn as sns
 
-    sns.set_theme()
+    sns.set_style("whitegrid")
     fig, ax = plt.subplots(1, 1)
     ax.plot(results.value[:, 0], results.value[:, 1], label="Estimate")
     ax.plot(
@@ -113,4 +113,6 @@ def main():
         axs[i].plot(results.stamp, results.error[:, i])
     axs[0].set_title("Estimation error")
     axs[1].set_xlabel("Time (s)")
+    axs[0].set_ylabel("x error (m)")
+    axs[1].set_ylabel("y error (m)")
     plt.show()

examples/ex_inertial_gps.py

@@ -13,7 +13,7 @@ def main():
     data = SimulatedInertialGPSDataset(t_start=0, t_end=20)
     gt_states = data.get_ground_truth()
     input_data = data.get_input_data()
-    meas_data = data.get_meas_data()
+    meas_data = data.get_measurement_data()
 
     # Filter initialization
     P0 = np.eye(15)

examples/ex_iterated_ekf_se3.py

@@ -11,7 +11,7 @@ def main():
     data = nav.lib.SimulatedPoseRangingDataset()
     gt_states = data.get_ground_truth()
     input_data = data.get_input_data()
-    meas_data = data.get_meas_data()
+    meas_data = data.get_measurement_data()
 
     # %% ###########################################################################
     # Perturb initial groundtruth state to initialize filter

examples/ex_ukf_se2.py → examples/ex_ukf_se3.py

@@ -25,7 +25,7 @@ def main():
     data = SimulatedPoseRangingDataset(x0=x0, Q=Q, noise_active=noise_active)
     state_true = data.get_ground_truth()
     input_data = data.get_input_data()
-    meas_data = data.get_meas_data()
+    meas_data = data.get_measurement_data()
     if noise_active:
         x0 = x0.plus(randvec(P0))
     # %% #######################################################################

navlie/filters.py

@@ -129,6 +129,10 @@ def predict(
         # If state has no time stamp, load from measurement.
         # usually only happens on estimator start-up
         if x.state.stamp is None:
+            if u.stamp is None:
+                raise ValueError(
+                    "Either state or input must have a time stamp"
+                )
             t_km1 = u.stamp
         else:
             t_km1 = x.state.stamp

navlie/lib/datasets.py

@@ -105,7 +105,7 @@ def get_ground_truth(self) -> List[SE3State]:
     def get_input_data(self) -> List[nav.Input]:
         return self.input_data
 
-    def get_meas_data(self) -> List[nav.Measurement]:
+    def get_measurement_data(self) -> List[nav.Measurement]:
         return self.meas_data
 
 
@@ -213,5 +213,5 @@ def get_ground_truth(self) -> List[IMUState]:
     def get_input_data(self) -> List[IMU]:
         return self.input_data
 
-    def get_meas_data(self) -> List[nav.Measurement]:
+    def get_measurement_data(self) -> List[nav.Measurement]:
         return self.meas_data

navlie/types.py

@@ -588,6 +588,6 @@ def get_input_data(self) -> List[Input]:
         pass
 
     @abstractmethod
-    def get_meas_data(self) -> List[Measurement]:
+    def get_measurement_data(self) -> List[Measurement]:
         """Returns a list of measurements."""
         pass