Pose2ISAM2Example.py

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"""
GTSAM Copyright 2010-2018, Georgia Tech Research Corporation,
Atlanta, Georgia 30332-0415
All Rights Reserved
Authors: Frank Dellaert, et al. (see THANKS for the full author list)

See LICENSE for the license information

Pose SLAM example using iSAM2 in the 2D plane.
Author: Jerred Chen, Yusuf Ali
Modeled after:
    - VisualISAM2Example by: Duy-Nguyen Ta (C++), Frank Dellaert (Python)
    - Pose2SLAMExample by: Alex Cunningham (C++), Kevin Deng & Frank Dellaert (Python)
"""

import math

import matplotlib.pyplot as plt
import numpy as np

import gtsam
import gtsam.utils.plot as gtsam_plot

def report_on_progress(graph: gtsam.NonlinearFactorGraph, current_estimate: gtsam.Values,
                        key: int):
    """Print and plot incremental progress of the robot for 2D Pose SLAM using iSAM2."""

    # Print the current estimates computed using iSAM2.
    print("*"*50 + f"\nInference after State {key+1}:\n")
    print(current_estimate)

    # Compute the marginals for all states in the graph.
    marginals = gtsam.Marginals(graph, current_estimate)

    # Plot the newly updated iSAM2 inference.
    fig = plt.figure(0)
    axes = fig.gca()
    plt.cla()

    i = 1
    while current_estimate.exists(i):
        gtsam_plot.plot_pose2(0, current_estimate.atPose2(i), 0.5, marginals.marginalCovariance(i))
        i += 1

    plt.axis('equal')
    axes.set_xlim(-1, 5)
    axes.set_ylim(-1, 3)
    plt.pause(1)

def determine_loop_closure(odom: np.ndarray, current_estimate: gtsam.Values,
    key: int, xy_tol=0.6, theta_tol=17) -> int:
    """Simple brute force approach which iterates through previous states
    and checks for loop closure.

    Args:
        odom: Vector representing noisy odometry (x, y, theta) measurement in the body frame.
        current_estimate: The current estimates computed by iSAM2.
        key: Key corresponding to the current state estimate of the robot.
        xy_tol: Optional argument for the x-y measurement tolerance, in meters.
        theta_tol: Optional argument for the theta measurement tolerance, in degrees.
    Returns:
        k: The key of the state which is helping add the loop closure constraint.
            If loop closure is not found, then None is returned.
    """
    if current_estimate:
        prev_est = current_estimate.atPose2(key+1)
        rotated_odom = prev_est.rotation().matrix() @ odom[:2]
        curr_xy = np.array([prev_est.x() + rotated_odom[0],
                            prev_est.y() + rotated_odom[1]])
        curr_theta = prev_est.theta() + odom[2]
        for k in range(1, key+1):
            pose_xy = np.array([current_estimate.atPose2(k).x(),
                                current_estimate.atPose2(k).y()])
            pose_theta = current_estimate.atPose2(k).theta()
            if (abs(pose_xy - curr_xy) <= xy_tol).all() and \
                (abs(pose_theta - curr_theta) <= theta_tol*np.pi/180):
                    return k

def Pose2SLAM_ISAM2_example():
    """Perform 2D SLAM given the ground truth changes in pose as well as
    simple loop closure detection."""
    plt.ion()

    # Declare the 2D translational standard deviations of the prior factor's Gaussian model, in meters.
    prior_xy_sigma = 0.3

    # Declare the 2D rotational standard deviation of the prior factor's Gaussian model, in degrees.
    prior_theta_sigma = 5

    # Declare the 2D translational standard deviations of the odometry factor's Gaussian model, in meters.
    odometry_xy_sigma = 0.2

    # Declare the 2D rotational standard deviation of the odometry factor's Gaussian model, in degrees.
    odometry_theta_sigma = 5

    # Although this example only uses linear measurements and Gaussian noise models, it is important
    # to note that iSAM2 can be utilized to its full potential during nonlinear optimization. This example
    # simply showcases how iSAM2 may be applied to a Pose2 SLAM problem.
    PRIOR_NOISE = gtsam.noiseModel.Diagonal.Sigmas(np.array([prior_xy_sigma,
                                                            prior_xy_sigma,
                                                            prior_theta_sigma*np.pi/180]))
    ODOMETRY_NOISE = gtsam.noiseModel.Diagonal.Sigmas(np.array([odometry_xy_sigma,
                                                                odometry_xy_sigma,
                                                                odometry_theta_sigma*np.pi/180]))

    # Create a Nonlinear factor graph as well as the data structure to hold state estimates.
    graph = gtsam.NonlinearFactorGraph()
    initial_estimate = gtsam.Values()

    # Create iSAM2 parameters which can adjust the threshold necessary to force relinearization and how many
    # update calls are required to perform the relinearization.
    parameters = gtsam.ISAM2Params()
    parameters.setRelinearizeThreshold(0.1)
    parameters.relinearizeSkip = 1
    isam = gtsam.ISAM2(parameters)

    # Create the ground truth odometry measurements of the robot during the trajectory.
    true_odometry = [(2, 0, 0),
                    (2, 0, math.pi/2),
                    (2, 0, math.pi/2),
                    (2, 0, math.pi/2),
                    (2, 0, math.pi/2)]

    # Corrupt the odometry measurements with gaussian noise to create noisy odometry measurements.
    odometry_measurements = [np.random.multivariate_normal(true_odom, ODOMETRY_NOISE.covariance())
                                for true_odom in true_odometry]

    # Add the prior factor to the factor graph, and poorly initialize the prior pose to demonstrate
    # iSAM2 incremental optimization.
    graph.push_back(gtsam.PriorFactorPose2(1, gtsam.Pose2(0, 0, 0), PRIOR_NOISE))
    initial_estimate.insert(1, gtsam.Pose2(0.5, 0.0, 0.2))

    # Initialize the current estimate which is used during the incremental inference loop.
    current_estimate = initial_estimate

    for i in range(len(true_odometry)):

        # Obtain the noisy odometry that is received by the robot and corrupted by gaussian noise.
        noisy_odom_x, noisy_odom_y, noisy_odom_theta = odometry_measurements[i]

        # Determine if there is loop closure based on the odometry measurement and the previous estimate of the state.
        loop = determine_loop_closure(odometry_measurements[i], current_estimate, i, xy_tol=0.8, theta_tol=25)

        # Add a binary factor in between two existing states if loop closure is detected.
        # Otherwise, add a binary factor between a newly observed state and the previous state.
        if loop:
            graph.push_back(gtsam.BetweenFactorPose2(i + 1, loop, 
                gtsam.Pose2(noisy_odom_x, noisy_odom_y, noisy_odom_theta), ODOMETRY_NOISE))
        else:
            graph.push_back(gtsam.BetweenFactorPose2(i + 1, i + 2, 
                gtsam.Pose2(noisy_odom_x, noisy_odom_y, noisy_odom_theta), ODOMETRY_NOISE))

            # Compute and insert the initialization estimate for the current pose using the noisy odometry measurement.
            computed_estimate = current_estimate.atPose2(i + 1).compose(gtsam.Pose2(noisy_odom_x,
                                                                                    noisy_odom_y,
                                                                                    noisy_odom_theta))
            initial_estimate.insert(i + 2, computed_estimate)

        # Perform incremental update to iSAM2's internal Bayes tree, optimizing only the affected variables.
        isam.update(graph, initial_estimate)
        current_estimate = isam.calculateEstimate()

        # Report all current state estimates from the iSAM2 optimzation.
        report_on_progress(graph, current_estimate, i)
        initial_estimate.clear()

    # Print the final covariance matrix for each pose after completing inference on the trajectory.
    marginals = gtsam.Marginals(graph, current_estimate)
    i = 1
    for i in range(1, len(true_odometry)+1):
        print(f"X{i} covariance:\n{marginals.marginalCovariance(i)}\n")
    
    plt.ioff()
    plt.show()


if __name__ == "__main__":
    Pose2SLAM_ISAM2_example()