Merge pull request #891 from borglab/origin/feature/python_examples
Pose SLAM Python examples using iSAM2release/4.3a0
Jerred Chen
GitHub
commit
0f8353f7ec
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"""
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GTSAM Copyright 2010-2018, Georgia Tech Research Corporation,
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Atlanta, Georgia 30332-0415
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All Rights Reserved
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Authors: Frank Dellaert, et al. (see THANKS for the full author list)
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See LICENSE for the license information
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Pose SLAM example using iSAM2 in the 2D plane.
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Author: Jerred Chen, Yusuf Ali
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Modeled after:
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- VisualISAM2Example by: Duy-Nguyen Ta (C++), Frank Dellaert (Python)
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- Pose2SLAMExample by: Alex Cunningham (C++), Kevin Deng & Frank Dellaert (Python)
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"""
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import math
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import matplotlib.pyplot as plt
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import numpy as np
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import gtsam
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import gtsam.utils.plot as gtsam_plot
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def report_on_progress(graph: gtsam.NonlinearFactorGraph, current_estimate: gtsam.Values,
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key: int):
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"""Print and plot incremental progress of the robot for 2D Pose SLAM using iSAM2."""
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# Print the current estimates computed using iSAM2.
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print("*"*50 + f"\nInference after State {key+1}:\n")
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print(current_estimate)
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# Compute the marginals for all states in the graph.
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marginals = gtsam.Marginals(graph, current_estimate)
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# Plot the newly updated iSAM2 inference.
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fig = plt.figure(0)
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axes = fig.gca()
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plt.cla()
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i = 1
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while current_estimate.exists(i):
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gtsam_plot.plot_pose2(0, current_estimate.atPose2(i), 0.5, marginals.marginalCovariance(i))
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i += 1
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plt.axis('equal')
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axes.set_xlim(-1, 5)
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axes.set_ylim(-1, 3)
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plt.pause(1)
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def determine_loop_closure(odom: np.ndarray, current_estimate: gtsam.Values,
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key: int, xy_tol=0.6, theta_tol=17) -> int:
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"""Simple brute force approach which iterates through previous states
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and checks for loop closure.
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Args:
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odom: Vector representing noisy odometry (x, y, theta) measurement in the body frame.
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current_estimate: The current estimates computed by iSAM2.
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key: Key corresponding to the current state estimate of the robot.
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xy_tol: Optional argument for the x-y measurement tolerance, in meters.
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theta_tol: Optional argument for the theta measurement tolerance, in degrees.
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Returns:
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k: The key of the state which is helping add the loop closure constraint.
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If loop closure is not found, then None is returned.
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"""
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if current_estimate:
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prev_est = current_estimate.atPose2(key+1)
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rotated_odom = prev_est.rotation().matrix() @ odom[:2]
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curr_xy = np.array([prev_est.x() + rotated_odom[0],
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prev_est.y() + rotated_odom[1]])
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curr_theta = prev_est.theta() + odom[2]
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for k in range(1, key+1):
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pose_xy = np.array([current_estimate.atPose2(k).x(),
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current_estimate.atPose2(k).y()])
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pose_theta = current_estimate.atPose2(k).theta()
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if (abs(pose_xy - curr_xy) <= xy_tol).all() and \
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(abs(pose_theta - curr_theta) <= theta_tol*np.pi/180):
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return k
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def Pose2SLAM_ISAM2_example():
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"""Perform 2D SLAM given the ground truth changes in pose as well as
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simple loop closure detection."""
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plt.ion()
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# Declare the 2D translational standard deviations of the prior factor's Gaussian model, in meters.
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prior_xy_sigma = 0.3
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# Declare the 2D rotational standard deviation of the prior factor's Gaussian model, in degrees.
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prior_theta_sigma = 5
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# Declare the 2D translational standard deviations of the odometry factor's Gaussian model, in meters.
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odometry_xy_sigma = 0.2
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# Declare the 2D rotational standard deviation of the odometry factor's Gaussian model, in degrees.
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odometry_theta_sigma = 5
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# Although this example only uses linear measurements and Gaussian noise models, it is important
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# to note that iSAM2 can be utilized to its full potential during nonlinear optimization. This example
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# simply showcases how iSAM2 may be applied to a Pose2 SLAM problem.
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PRIOR_NOISE = gtsam.noiseModel.Diagonal.Sigmas(np.array([prior_xy_sigma,
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prior_xy_sigma,
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prior_theta_sigma*np.pi/180]))
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ODOMETRY_NOISE = gtsam.noiseModel.Diagonal.Sigmas(np.array([odometry_xy_sigma,
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odometry_xy_sigma,
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odometry_theta_sigma*np.pi/180]))
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# Create a Nonlinear factor graph as well as the data structure to hold state estimates.
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graph = gtsam.NonlinearFactorGraph()
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initial_estimate = gtsam.Values()
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# Create iSAM2 parameters which can adjust the threshold necessary to force relinearization and how many
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# update calls are required to perform the relinearization.
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parameters = gtsam.ISAM2Params()
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parameters.setRelinearizeThreshold(0.1)
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parameters.setRelinearizeSkip(1)
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isam = gtsam.ISAM2(parameters)
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# Create the ground truth odometry measurements of the robot during the trajectory.
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true_odometry = [(2, 0, 0),
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(2, 0, math.pi/2),
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(2, 0, math.pi/2),
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(2, 0, math.pi/2),
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(2, 0, math.pi/2)]
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# Corrupt the odometry measurements with gaussian noise to create noisy odometry measurements.
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odometry_measurements = [np.random.multivariate_normal(true_odom, ODOMETRY_NOISE.covariance())
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for true_odom in true_odometry]
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# Add the prior factor to the factor graph, and poorly initialize the prior pose to demonstrate
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# iSAM2 incremental optimization.
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graph.push_back(gtsam.PriorFactorPose2(1, gtsam.Pose2(0, 0, 0), PRIOR_NOISE))
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initial_estimate.insert(1, gtsam.Pose2(0.5, 0.0, 0.2))
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# Initialize the current estimate which is used during the incremental inference loop.
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current_estimate = initial_estimate
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for i in range(len(true_odometry)):
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# Obtain the noisy odometry that is received by the robot and corrupted by gaussian noise.
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noisy_odom_x, noisy_odom_y, noisy_odom_theta = odometry_measurements[i]
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# Determine if there is loop closure based on the odometry measurement and the previous estimate of the state.
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loop = determine_loop_closure(odometry_measurements[i], current_estimate, i, xy_tol=0.8, theta_tol=25)
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# Add a binary factor in between two existing states if loop closure is detected.
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# Otherwise, add a binary factor between a newly observed state and the previous state.
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if loop:
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graph.push_back(gtsam.BetweenFactorPose2(i + 1, loop,
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gtsam.Pose2(noisy_odom_x, noisy_odom_y, noisy_odom_theta), ODOMETRY_NOISE))
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else:
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graph.push_back(gtsam.BetweenFactorPose2(i + 1, i + 2,
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gtsam.Pose2(noisy_odom_x, noisy_odom_y, noisy_odom_theta), ODOMETRY_NOISE))
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# Compute and insert the initialization estimate for the current pose using the noisy odometry measurement.
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computed_estimate = current_estimate.atPose2(i + 1).compose(gtsam.Pose2(noisy_odom_x,
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noisy_odom_y,
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noisy_odom_theta))
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initial_estimate.insert(i + 2, computed_estimate)
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# Perform incremental update to iSAM2's internal Bayes tree, optimizing only the affected variables.
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isam.update(graph, initial_estimate)
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current_estimate = isam.calculateEstimate()
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# Report all current state estimates from the iSAM2 optimzation.
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report_on_progress(graph, current_estimate, i)
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initial_estimate.clear()
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# Print the final covariance matrix for each pose after completing inference on the trajectory.
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marginals = gtsam.Marginals(graph, current_estimate)
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i = 1
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for i in range(1, len(true_odometry)+1):
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print(f"X{i} covariance:\n{marginals.marginalCovariance(i)}\n")
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plt.ioff()
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plt.show()
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if __name__ == "__main__":
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Pose2SLAM_ISAM2_example()
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"""
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GTSAM Copyright 2010-2018, Georgia Tech Research Corporation,
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Atlanta, Georgia 30332-0415
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All Rights Reserved
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Authors: Frank Dellaert, et al. (see THANKS for the full author list)
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See LICENSE for the license information
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Pose SLAM example using iSAM2 in 3D space.
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Author: Jerred Chen
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Modeled after:
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- VisualISAM2Example by: Duy-Nguyen Ta (C++), Frank Dellaert (Python)
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- Pose2SLAMExample by: Alex Cunningham (C++), Kevin Deng & Frank Dellaert (Python)
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"""
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from typing import List
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import matplotlib.pyplot as plt
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import numpy as np
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import gtsam
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import gtsam.utils.plot as gtsam_plot
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def report_on_progress(graph: gtsam.NonlinearFactorGraph, current_estimate: gtsam.Values,
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key: int):
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"""Print and plot incremental progress of the robot for 2D Pose SLAM using iSAM2."""
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# Print the current estimates computed using iSAM2.
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print("*"*50 + f"\nInference after State {key+1}:\n")
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print(current_estimate)
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# Compute the marginals for all states in the graph.
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marginals = gtsam.Marginals(graph, current_estimate)
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# Plot the newly updated iSAM2 inference.
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fig = plt.figure(0)
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axes = fig.gca(projection='3d')
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plt.cla()
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i = 1
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while current_estimate.exists(i):
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gtsam_plot.plot_pose3(0, current_estimate.atPose3(i), 10,
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marginals.marginalCovariance(i))
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i += 1
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axes.set_xlim3d(-30, 45)
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axes.set_ylim3d(-30, 45)
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axes.set_zlim3d(-30, 45)
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plt.pause(1)
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def create_poses() -> List[gtsam.Pose3]:
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"""Creates ground truth poses of the robot."""
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P0 = np.array([[1, 0, 0, 0],
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[0, 1, 0, 0],
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[0, 0, 1, 0],
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[0, 0, 0, 1]])
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P1 = np.array([[0, -1, 0, 15],
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[1, 0, 0, 15],
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[0, 0, 1, 20],
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[0, 0, 0, 1]])
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P2 = np.array([[np.cos(np.pi/4), 0, np.sin(np.pi/4), 30],
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[0, 1, 0, 30],
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[-np.sin(np.pi/4), 0, np.cos(np.pi/4), 30],
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[0, 0, 0, 1]])
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P3 = np.array([[0, 1, 0, 30],
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[0, 0, -1, 0],
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[-1, 0, 0, -15],
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[0, 0, 0, 1]])
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P4 = np.array([[-1, 0, 0, 0],
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[0, -1, 0, -10],
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[0, 0, 1, -10],
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[0, 0, 0, 1]])
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P5 = P0[:]
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return [gtsam.Pose3(P0), gtsam.Pose3(P1), gtsam.Pose3(P2),
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gtsam.Pose3(P3), gtsam.Pose3(P4), gtsam.Pose3(P5)]
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def determine_loop_closure(odom_tf: gtsam.Pose3, current_estimate: gtsam.Values,
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key: int, xyz_tol=0.6, rot_tol=17) -> int:
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"""Simple brute force approach which iterates through previous states
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and checks for loop closure.
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Args:
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odom_tf: The noisy odometry transformation measurement in the body frame.
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current_estimate: The current estimates computed by iSAM2.
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key: Key corresponding to the current state estimate of the robot.
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xyz_tol: Optional argument for the translational tolerance, in meters.
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rot_tol: Optional argument for the rotational tolerance, in degrees.
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Returns:
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k: The key of the state which is helping add the loop closure constraint.
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If loop closure is not found, then None is returned.
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"""
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if current_estimate:
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prev_est = current_estimate.atPose3(key+1)
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curr_est = prev_est.compose(odom_tf)
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for k in range(1, key+1):
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pose = current_estimate.atPose3(k)
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if (abs(pose.matrix()[:3,:3] - curr_est.matrix()[:3,:3]) <= rot_tol*np.pi/180).all() and \
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(abs(pose.matrix()[:3,3] - curr_est.matrix()[:3,3]) <= xyz_tol).all():
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return k
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def Pose3_ISAM2_example():
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"""Perform 3D SLAM given ground truth poses as well as simple
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loop closure detection."""
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plt.ion()
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# Declare the 3D translational standard deviations of the prior factor's Gaussian model, in meters.
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prior_xyz_sigma = 0.3
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# Declare the 3D rotational standard deviations of the prior factor's Gaussian model, in degrees.
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prior_rpy_sigma = 5
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# Declare the 3D translational standard deviations of the odometry factor's Gaussian model, in meters.
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odometry_xyz_sigma = 0.2
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# Declare the 3D rotational standard deviations of the odometry factor's Gaussian model, in degrees.
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odometry_rpy_sigma = 5
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# Although this example only uses linear measurements and Gaussian noise models, it is important
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# to note that iSAM2 can be utilized to its full potential during nonlinear optimization. This example
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# simply showcases how iSAM2 may be applied to a Pose2 SLAM problem.
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PRIOR_NOISE = gtsam.noiseModel.Diagonal.Sigmas(np.array([prior_rpy_sigma*np.pi/180,
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prior_rpy_sigma*np.pi/180,
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prior_rpy_sigma*np.pi/180,
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prior_xyz_sigma,
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prior_xyz_sigma,
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prior_xyz_sigma]))
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ODOMETRY_NOISE = gtsam.noiseModel.Diagonal.Sigmas(np.array([odometry_rpy_sigma*np.pi/180,
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odometry_rpy_sigma*np.pi/180,
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odometry_rpy_sigma*np.pi/180,
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odometry_xyz_sigma,
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odometry_xyz_sigma,
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odometry_xyz_sigma]))
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# Create a Nonlinear factor graph as well as the data structure to hold state estimates.
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graph = gtsam.NonlinearFactorGraph()
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initial_estimate = gtsam.Values()
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# Create iSAM2 parameters which can adjust the threshold necessary to force relinearization and how many
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# update calls are required to perform the relinearization.
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parameters = gtsam.ISAM2Params()
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parameters.setRelinearizeThreshold(0.1)
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parameters.setRelinearizeSkip(1)
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isam = gtsam.ISAM2(parameters)
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# Create the ground truth poses of the robot trajectory.
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true_poses = create_poses()
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# Create the ground truth odometry transformations, xyz translations, and roll-pitch-yaw rotations
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# between each robot pose in the trajectory.
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odometry_tf = [true_poses[i-1].transformPoseTo(true_poses[i]) for i in range(1, len(true_poses))]
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odometry_xyz = [(odometry_tf[i].x(), odometry_tf[i].y(), odometry_tf[i].z()) for i in range(len(odometry_tf))]
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odometry_rpy = [odometry_tf[i].rotation().rpy() for i in range(len(odometry_tf))]
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# Corrupt xyz translations and roll-pitch-yaw rotations with gaussian noise to create noisy odometry measurements.
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noisy_measurements = [np.random.multivariate_normal(np.hstack((odometry_rpy[i],odometry_xyz[i])), \
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ODOMETRY_NOISE.covariance()) for i in range(len(odometry_tf))]
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# Add the prior factor to the factor graph, and poorly initialize the prior pose to demonstrate
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# iSAM2 incremental optimization.
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graph.push_back(gtsam.PriorFactorPose3(1, true_poses[0], PRIOR_NOISE))
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initial_estimate.insert(1, true_poses[0].compose(gtsam.Pose3(
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gtsam.Rot3.Rodrigues(-0.1, 0.2, 0.25), gtsam.Point3(0.05, -0.10, 0.20))))
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# Initialize the current estimate which is used during the incremental inference loop.
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current_estimate = initial_estimate
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for i in range(len(odometry_tf)):
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# Obtain the noisy translation and rotation that is received by the robot and corrupted by gaussian noise.
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noisy_odometry = noisy_measurements[i]
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# Compute the noisy odometry transformation according to the xyz translation and roll-pitch-yaw rotation.
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noisy_tf = gtsam.Pose3(gtsam.Rot3.RzRyRx(noisy_odometry[:3]), noisy_odometry[3:6].reshape(-1,1))
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# Determine if there is loop closure based on the odometry measurement and the previous estimate of the state.
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loop = determine_loop_closure(noisy_tf, current_estimate, i, xyz_tol=18, rot_tol=30)
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# Add a binary factor in between two existing states if loop closure is detected.
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# Otherwise, add a binary factor between a newly observed state and the previous state.
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if loop:
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graph.push_back(gtsam.BetweenFactorPose3(i + 1, loop, noisy_tf, ODOMETRY_NOISE))
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else:
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graph.push_back(gtsam.BetweenFactorPose3(i + 1, i + 2, noisy_tf, ODOMETRY_NOISE))
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# Compute and insert the initialization estimate for the current pose using a noisy odometry measurement.
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noisy_estimate = current_estimate.atPose3(i + 1).compose(noisy_tf)
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initial_estimate.insert(i + 2, noisy_estimate)
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# Perform incremental update to iSAM2's internal Bayes tree, optimizing only the affected variables.
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isam.update(graph, initial_estimate)
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current_estimate = isam.calculateEstimate()
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# Report all current state estimates from the iSAM2 optimization.
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report_on_progress(graph, current_estimate, i)
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initial_estimate.clear()
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# Print the final covariance matrix for each pose after completing inference.
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marginals = gtsam.Marginals(graph, current_estimate)
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i = 1
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while current_estimate.exists(i):
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print(f"X{i} covariance:\n{marginals.marginalCovariance(i)}\n")
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i += 1
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plt.ioff()
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plt.show()
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if __name__ == '__main__':
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Pose3_ISAM2_example()
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