renamed ImuFactorExample2 to ImuFactorISAM2Example
Varun Agrawal
parent
d79ddb6858
commit
bfbb6cb28d
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@ -1,6 +1,6 @@
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"""
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iSAM2 example with ImuFactor.
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Author: Robert Truax (C++), Frank Dellaert (Python)
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Author: Frank Dellaert, Varun Agrawal
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"""
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# pylint: disable=invalid-name, E1101
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@ -8,10 +8,11 @@ from __future__ import print_function
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import math
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import gtsam
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import gtsam.utils.plot as gtsam_plot
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import matplotlib.pyplot as plt
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import numpy as np
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from mpl_toolkits.mplot3d import Axes3D # pylint: disable=W0611
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import gtsam
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from gtsam import (ISAM2, BetweenFactorConstantBias, Cal3_S2,
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ConstantTwistScenario, ImuFactor, NonlinearFactorGraph,
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PinholeCameraCal3_S2, Point3, Pose3,
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@ -20,7 +21,7 @@ from gtsam import (ISAM2, BetweenFactorConstantBias, Cal3_S2,
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from gtsam import symbol_shorthand_B as B
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from gtsam import symbol_shorthand_V as V
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from gtsam import symbol_shorthand_X as X
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from mpl_toolkits.mplot3d import Axes3D # pylint: disable=W0611
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from gtsam.utils import plot
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def vector3(x, y, z):
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@ -28,35 +29,13 @@ def vector3(x, y, z):
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return np.array([x, y, z], dtype=np.float)
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def ISAM2_plot(values, fignum=0):
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"""Plot poses."""
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fig = plt.figure(fignum)
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axes = fig.gca(projection='3d')
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plt.cla()
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i = 0
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min_bounds = 0, 0, 0
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max_bounds = 0, 0, 0
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while values.exists(X(i)):
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pose_i = values.atPose3(X(i))
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gtsam_plot.plot_pose3(fignum, pose_i, 10)
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position = pose_i.translation().vector()
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min_bounds = [min(v1, v2) for (v1, v2) in zip(position, min_bounds)]
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max_bounds = [max(v1, v2) for (v1, v2) in zip(position, max_bounds)]
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# max_bounds = min(pose_i.x(), max_bounds[0]), 0, 0
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i += 1
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# draw
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axes.set_xlim3d(min_bounds[0]-1, max_bounds[0]+1)
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axes.set_ylim3d(min_bounds[1]-1, max_bounds[1]+1)
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axes.set_zlim3d(min_bounds[2]-1, max_bounds[2]+1)
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plt.pause(1)
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g = 9.81
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kGravity = vector3(0, 0, -g)
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def preintegration_parameters():
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# IMU preintegration parameters
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# Default Params for a Z-up navigation frame, such as ENU: gravity points along negative Z-axis
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g = 9.81
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n_gravity = vector3(0, 0, -g)
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PARAMS = gtsam.PreintegrationParams.MakeSharedU(g)
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I = np.eye(3)
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PARAMS.setAccelerometerCovariance(I * 0.1)
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@ -69,29 +48,45 @@ BIAS_COVARIANCE = gtsam.noiseModel_Isotropic.Variance(6, 0.1)
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DELTA = Pose3(Rot3.Rodrigues(0, 0, 0),
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Point3(0.05, -0.10, 0.20))
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return PARAMS, BIAS_COVARIANCE, DELTA
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def IMU_example():
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"""Run iSAM 2 example with IMU factor."""
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# Start with a camera on x-axis looking at origin
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radius = 30
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def get_camera(radius):
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up = Point3(0, 0, 1)
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target = Point3(0, 0, 0)
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position = Point3(radius, 0, 0)
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camera = PinholeCameraCal3_S2.Lookat(position, target, up, Cal3_S2())
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pose_0 = camera.pose()
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return camera
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def get_scenario(radius, pose_0, angular_velocity, delta_t):
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"""Create the set of ground-truth landmarks and poses"""
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# Create the set of ground-truth landmarks and poses
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angular_velocity = math.radians(180) # rad/sec
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delta_t = 1.0/18 # makes for 10 degrees per step
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angular_velocity_vector = vector3(0, -angular_velocity, 0)
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linear_velocity_vector = vector3(radius * angular_velocity, 0, 0)
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scenario = ConstantTwistScenario(
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angular_velocity_vector, linear_velocity_vector, pose_0)
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return scenario
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def IMU_example():
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"""Run iSAM 2 example with IMU factor."""
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# Start with a camera on x-axis looking at origin
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radius = 30
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camera = get_camera(radius)
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pose_0 = camera.pose()
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delta_t = 1.0/18 # makes for 10 degrees per step
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angular_velocity = math.radians(180) # rad/sec
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scenario = get_scenario(radius, pose_0, angular_velocity, delta_t)
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PARAMS, BIAS_COVARIANCE, DELTA = preintegration_parameters()
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# Create a factor graph
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newgraph = NonlinearFactorGraph()
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graph = NonlinearFactorGraph()
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# Create (incremental) ISAM2 solver
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isam = ISAM2()
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@ -104,21 +99,21 @@ def IMU_example():
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# 30cm std on x,y,z 0.1 rad on roll,pitch,yaw
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noise = gtsam.noiseModel_Diagonal.Sigmas(
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np.array([0.1, 0.1, 0.1, 0.3, 0.3, 0.3]))
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newgraph.push_back(PriorFactorPose3(X(0), pose_0, noise))
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graph.push_back(PriorFactorPose3(X(0), pose_0, noise))
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# Add imu priors
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biasKey = B(0)
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biasnoise = gtsam.noiseModel_Isotropic.Sigma(6, 0.1)
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biasprior = PriorFactorConstantBias(biasKey, gtsam.imuBias_ConstantBias(),
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biasnoise)
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newgraph.push_back(biasprior)
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graph.push_back(biasprior)
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initialEstimate.insert(biasKey, gtsam.imuBias_ConstantBias())
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velnoise = gtsam.noiseModel_Isotropic.Sigma(3, 0.1)
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# Calculate with correct initial velocity
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n_velocity = vector3(0, angular_velocity * radius, 0)
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velprior = PriorFactorVector(V(0), n_velocity, velnoise)
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newgraph.push_back(velprior)
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graph.push_back(velprior)
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initialEstimate.insert(V(0), n_velocity)
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accum = gtsam.PreintegratedImuMeasurements(PARAMS)
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@ -140,33 +135,36 @@ def IMU_example():
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biasKey += 1
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factor = BetweenFactorConstantBias(
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biasKey - 1, biasKey, gtsam.imuBias_ConstantBias(), BIAS_COVARIANCE)
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newgraph.add(factor)
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graph.add(factor)
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initialEstimate.insert(biasKey, gtsam.imuBias_ConstantBias())
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# Predict acceleration and gyro measurements in (actual) body frame
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nRb = scenario.rotation(t).matrix()
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bRn = np.transpose(nRb)
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measuredAcc = scenario.acceleration_b(t) - np.dot(bRn, n_gravity)
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measuredAcc = scenario.acceleration_b(t) - np.dot(bRn, kGravity)
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measuredOmega = scenario.omega_b(t)
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accum.integrateMeasurement(measuredAcc, measuredOmega, delta_t)
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# Add Imu Factor
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imufac = ImuFactor(X(i - 1), V(i - 1), X(i), V(i), biasKey, accum)
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newgraph.add(imufac)
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graph.add(imufac)
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# insert new velocity, which is wrong
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initialEstimate.insert(V(i), n_velocity)
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accum.resetIntegration()
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# Incremental solution
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isam.update(newgraph, initialEstimate)
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isam.update(graph, initialEstimate)
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result = isam.calculateEstimate()
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ISAM2_plot(result)
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plot.plot_incremental_trajectory(0, result,
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start=i, scale=3, time_interval=0.01)
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# reset
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newgraph = NonlinearFactorGraph()
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graph = NonlinearFactorGraph()
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initialEstimate.clear()
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plt.show()
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if __name__ == '__main__':
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IMU_example()
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