Cleaned up planar SLAM example and created new Localization example
Frank Dellaert
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/* ----------------------------------------------------------------------------
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* GTSAM Copyright 2010, 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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* -------------------------------------------------------------------------- */
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/**
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* @file LocalizationExample.cpp
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* @brief Simple robot localization example
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* @author Frank Dellaert
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*/
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// pull in the 2D PoseSLAM domain with all typedefs and helper functions defined
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#include <gtsam/slam/pose2SLAM.h>
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using namespace std;
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using namespace gtsam;
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/**
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* Example of a simple 2D localization example
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* - Robot poses are facing along the X axis (horizontal, to the right in 2D)
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* - The robot moves 2 meters each step
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* - We have full odometry between poses
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*/
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int main(int argc, char** argv) {
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// create the graph (defined in pose2SLAM.h, derived from NonlinearFactorGraph)
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pose2SLAM::Graph graph;
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// add a Gaussian prior on pose x_1
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Pose2 priorMean(0.0, 0.0, 0.0); // prior mean is at origin
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SharedDiagonal priorNoise(Vector_(3, 0.3, 0.3, 0.1)); // 30cm std on x,y, 0.1 rad on theta
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graph.addPrior(1, priorMean, priorNoise); // add directly to graph
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// add two odometry factors
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Pose2 odometry(2.0, 0.0, 0.0); // create a measurement for both factors (the same in this case)
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SharedDiagonal odometryNoise(Vector_(3, 0.2, 0.2, 0.1)); // 20cm std on x,y, 0.1 rad on theta
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graph.addOdometry(1, 2, odometry, odometryNoise);
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graph.addOdometry(2, 3, odometry, odometryNoise);
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// print
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graph.print("\nFactor graph:\n");
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// create (deliberatly inaccurate) initial estimate
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pose2SLAM::Values initialEstimate;
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initialEstimate.insertPose(1, Pose2(0.5, 0.0, 0.2));
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initialEstimate.insertPose(2, Pose2(2.3, 0.1,-0.2));
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initialEstimate.insertPose(3, Pose2(4.1, 0.1, 0.1));
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initialEstimate.print("\nInitial estimate:\n ");
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// optimize using Levenberg-Marquardt optimization with an ordering from colamd
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pose2SLAM::Values result = graph.optimize(initialEstimate);
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result.print("\nFinal result:\n ");
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return 0;
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}
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@ -15,64 +15,57 @@
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* @author Alex Cunningham
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*/
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#include <cmath>
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#include <iostream>
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// pull in the planar SLAM domain with all typedefs and helper functions defined
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#include <gtsam/slam/planarSLAM.h>
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#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
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using namespace std;
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using namespace gtsam;
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using namespace planarSLAM;
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/**
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* In this version of the system we make the following assumptions:
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* - All values are axis aligned
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* - Robot poses are facing along the X axis (horizontal, to the right in images)
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* Example of a simple 2D planar slam example with landmarls
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* - The robot and landmarks are on a 2 meter grid
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* - Robot poses are facing along the X axis (horizontal, to the right in 2D)
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* - The robot moves 2 meters each step
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* - We have full odometry between poses
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* - We have bearing and range information for measurements
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* - We have full odometry for measurements
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* - The robot and landmarks are on a grid, moving 2 meters each step
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* - Landmarks are 2 meters away from the robot trajectory
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*/
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int main(int argc, char** argv) {
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// create graph container and add factors to it
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Graph graph;
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// create the graph (defined in planarSlam.h, derived from NonlinearFactorGraph)
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planarSLAM::Graph graph;
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/* add prior */
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// gaussian for prior
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SharedDiagonal prior_model = noiseModel::Diagonal::Sigmas(Vector_(3, 0.3, 0.3, 0.1));
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Pose2 prior_measurement(0.0, 0.0, 0.0); // prior at origin
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graph.addPrior(1, prior_measurement, prior_model); // add directly to graph
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// add a Gaussian prior on pose x_1
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Pose2 priorMean(0.0, 0.0, 0.0); // prior mean is at origin
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SharedDiagonal priorNoise(Vector_(3, 0.3, 0.3, 0.1)); // 30cm std on x,y, 0.1 rad on theta
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graph.addPrior(1, priorMean, priorNoise); // add directly to graph
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/* add odometry */
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// general noisemodel for odometry
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SharedDiagonal odom_model = noiseModel::Diagonal::Sigmas(Vector_(3, 0.2, 0.2, 0.1));
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Pose2 odom_measurement(2.0, 0.0, 0.0); // create a measurement for both factors (the same in this case)
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graph.addOdometry(1, 2, odom_measurement, odom_model);
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graph.addOdometry(2, 3, odom_measurement, odom_model);
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// add two odometry factors
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Pose2 odometry(2.0, 0.0, 0.0); // create a measurement for both factors (the same in this case)
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SharedDiagonal odometryNoise(Vector_(3, 0.2, 0.2, 0.1)); // 20cm std on x,y, 0.1 rad on theta
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graph.addOdometry(1, 2, odometry, odometryNoise);
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graph.addOdometry(2, 3, odometry, odometryNoise);
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/* add measurements */
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// general noisemodel for measurements
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SharedDiagonal meas_model = noiseModel::Diagonal::Sigmas(Vector_(2, 0.1, 0.2));
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// create a noise model for the landmark measurements
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SharedDiagonal measurementNoise(Vector_(2, 0.1, 0.2)); // 0.1 rad std on bearing, 20cm on range
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// create the measurement values - indices are (pose id, landmark id)
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Rot2 bearing11 = Rot2::fromDegrees(45),
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bearing21 = Rot2::fromDegrees(90),
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bearing32 = Rot2::fromDegrees(90);
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bearing21 = Rot2::fromDegrees(90),
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bearing32 = Rot2::fromDegrees(90);
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double range11 = sqrt(4+4),
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range21 = 2.0,
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range32 = 2.0;
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range21 = 2.0,
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range32 = 2.0;
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// create bearing/range factors and add them
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graph.addBearingRange(1, 1, bearing11, range11, meas_model);
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graph.addBearingRange(2, 1, bearing21, range21, meas_model);
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graph.addBearingRange(3, 2, bearing32, range32, meas_model);
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// add bearing/range factors (created by "addBearingRange")
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graph.addBearingRange(1, 1, bearing11, range11, measurementNoise);
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graph.addBearingRange(2, 1, bearing21, range21, measurementNoise);
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graph.addBearingRange(3, 2, bearing32, range32, measurementNoise);
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graph.print("full graph");
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// print
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graph.print("Factor graph");
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// initialize to noisy points
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// create (deliberatly inaccurate) initial estimate
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planarSLAM::Values initialEstimate;
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initialEstimate.insertPose(1, Pose2(0.5, 0.0, 0.2));
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initialEstimate.insertPose(2, Pose2(2.3, 0.1,-0.2));
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@ -80,11 +73,11 @@ int main(int argc, char** argv) {
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initialEstimate.insertPoint(1, Point2(1.8, 2.1));
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initialEstimate.insertPoint(2, Point2(4.1, 1.8));
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initialEstimate.print("initial estimate");
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initialEstimate.print("Initial estimate:\n ");
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// optimize using Levenberg-Marquardt optimization with an ordering from colamd
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planarSLAM::Values result = LevenbergMarquardtOptimizer(graph, initialEstimate).optimize();
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result.print("final result");
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planarSLAM::Values result = graph.optimize(initialEstimate);
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result.print("Final result:\n ");
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return 0;
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}
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