76 lines
2.3 KiB
C++
76 lines
2.3 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 GNCExample.cpp
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* @brief Simple example showcasing a Graduated Non-Convexity based solver
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* @author Achintya Mohan
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*/
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/**
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* A simple 2D pose graph optimization example
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* - The robot is initially at origin (0.0, 0.0, 0.0)
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* - We have full odometry measurements for 2 motions
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* - The robot first moves to (1.0, 0.0, 0.1) and then to (1.0, 1.0, 0.2)
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*/
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#include <gtsam/geometry/Pose2.h>
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#include <gtsam/nonlinear/GncOptimizer.h>
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#include <gtsam/nonlinear/GncParams.h>
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#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
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#include <gtsam/nonlinear/LevenbergMarquardtParams.h>
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#include <gtsam/nonlinear/NonlinearFactorGraph.h>
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#include <gtsam/slam/BetweenFactor.h>
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#include <iostream>
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using namespace std;
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using namespace gtsam;
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int main() {
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cout << "Graduated Non-Convexity Example\n";
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NonlinearFactorGraph graph;
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// Add a prior to the first point, set to the origin
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auto priorNoise = noiseModel::Isotropic::Sigma(3, 0.1);
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graph.addPrior(1, Pose2(0.0, 0.0, 0.0), priorNoise);
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// Add additional factors, noise models must be Gaussian
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Pose2 x1(1.0, 0.0, 0.1);
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graph.emplace_shared<BetweenFactor<Pose2>>(1, 2, x1, noiseModel::Isotropic::Sigma(3, 0.2));
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Pose2 x2(0.0, 1.0, 0.1);
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graph.emplace_shared<BetweenFactor<Pose2>>(2, 3, x2, noiseModel::Isotropic::Sigma(3, 0.4));
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// Initial estimates
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Values initial;
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initial.insert(1, Pose2(0.2, 0.5, -0.1));
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initial.insert(2, Pose2(0.8, 0.3, 0.1));
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initial.insert(3, Pose2(0.8, 0.2, 0.3));
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// Set options for the non-minimal solver
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LevenbergMarquardtParams lmParams;
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lmParams.setMaxIterations(1000);
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lmParams.setRelativeErrorTol(1e-5);
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// Set GNC-specific options
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GncParams<LevenbergMarquardtParams> gncParams(lmParams);
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gncParams.setLossType(GncLossType::TLS);
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// Optimize the graph and print results
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GncOptimizer<GncParams<LevenbergMarquardtParams>> optimizer(graph, initial, gncParams);
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Values result = optimizer.optimize();
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result.print("Final Result:");
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return 0;
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}
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