100 lines
4.3 KiB
C++
100 lines
4.3 KiB
C++
/* ----------------------------------------------------------------------------
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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 OdometryExample.cpp
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* @brief Simple robot motion example, with prior and two odometry measurements
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* @author Frank Dellaert
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*/
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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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// We will use Pose2 variables (x, y, theta) to represent the robot positions
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#include <gtsam/geometry/Pose2.h>
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// In GTSAM, measurement functions are represented as 'factors'. Several common factors
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// have been provided with the library for solving robotics/SLAM/Bundle Adjustment problems.
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// Here we will use Between factors for the relative motion described by odometry measurements.
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// Also, we will initialize the robot at the origin using a Prior factor.
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#include <gtsam/slam/PriorFactor.h>
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#include <gtsam/slam/BetweenFactor.h>
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// When the factors are created, we will add them to a Factor Graph. As the factors we are using
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// are nonlinear factors, we will need a Nonlinear Factor Graph.
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#include <gtsam/nonlinear/NonlinearFactorGraph.h>
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// Finally, once all of the factors have been added to our factor graph, we will want to
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// solve/optimize to graph to find the best (Maximum A Posteriori) set of variable values.
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// GTSAM includes several nonlinear optimizers to perform this step. Here we will use the
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// Levenberg-Marquardt solver
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#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
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// Once the optimized values have been calculated, we can also calculate the marginal covariance
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// of desired variables
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#include <gtsam/nonlinear/Marginals.h>
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// The nonlinear solvers within GTSAM are iterative solvers, meaning they linearize the
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// nonlinear functions around an initial linearization point, then solve the linear system
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// to update the linearization point. This happens repeatedly until the solver converges
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// to a consistent set of variable values. This requires us to specify an initial guess
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// for each variable, held in a Values container.
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#include <gtsam/nonlinear/Values.h>
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using namespace std;
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using namespace gtsam;
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int main(int argc, char** argv) {
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// Create an empty nonlinear factor graph
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NonlinearFactorGraph graph;
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// Add a prior on the first pose, setting it to the origin
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// A prior factor consists of a mean and a noise model (covariance matrix)
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Pose2 priorMean(0.0, 0.0, 0.0); // prior at origin
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noiseModel::Diagonal::shared_ptr priorNoise = noiseModel::Diagonal::Sigmas(Vector3(0.3, 0.3, 0.1));
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graph.emplace_shared<PriorFactor<Pose2> >(1, priorMean, priorNoise);
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// Add odometry factors
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Pose2 odometry(2.0, 0.0, 0.0);
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// For simplicity, we will use the same noise model for each odometry factor
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noiseModel::Diagonal::shared_ptr odometryNoise = noiseModel::Diagonal::Sigmas(Vector3(0.2, 0.2, 0.1));
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// Create odometry (Between) factors between consecutive poses
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graph.emplace_shared<BetweenFactor<Pose2> >(1, 2, odometry, odometryNoise);
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graph.emplace_shared<BetweenFactor<Pose2> >(2, 3, odometry, odometryNoise);
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graph.print("\nFactor Graph:\n"); // print
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// Create the data structure to hold the initialEstimate estimate to the solution
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// For illustrative purposes, these have been deliberately set to incorrect values
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Values initial;
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initial.insert(1, Pose2(0.5, 0.0, 0.2));
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initial.insert(2, Pose2(2.3, 0.1, -0.2));
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initial.insert(3, Pose2(4.1, 0.1, 0.1));
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initial.print("\nInitial Estimate:\n"); // print
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// optimize using Levenberg-Marquardt optimization
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Values result = LevenbergMarquardtOptimizer(graph, initial).optimize();
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result.print("Final Result:\n");
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// Calculate and print marginal covariances for all variables
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cout.precision(2);
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Marginals marginals(graph, result);
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cout << "x1 covariance:\n" << marginals.marginalCovariance(1) << endl;
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cout << "x2 covariance:\n" << marginals.marginalCovariance(2) << endl;
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cout << "x3 covariance:\n" << marginals.marginalCovariance(3) << endl;
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return 0;
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}
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