Merge pull request #1634 from achintyamohan/develop

Add cpp example for GNC
release/4.3a0
Varun Agrawal 2023-09-12 07:39:53 -04:00 committed by GitHub
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/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, 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
* -------------------------------------------------------------------------- */
/**
* @file GNCExample.cpp
* @brief Simple example showcasing a Graduated Non-Convexity based solver
* @author Achintya Mohan
*/
/**
* A simple 2D pose graph optimization example
* - The robot is initially at origin (0.0, 0.0, 0.0)
* - We have full odometry measurements for 2 motions
* - The robot first moves to (1.0, 0.0, 0.1) and then to (1.0, 1.0, 0.2)
*/
#include <gtsam/geometry/Pose2.h>
#include <gtsam/nonlinear/GncOptimizer.h>
#include <gtsam/nonlinear/GncParams.h>
#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
#include <gtsam/nonlinear/LevenbergMarquardtParams.h>
#include <gtsam/nonlinear/NonlinearFactorGraph.h>
#include <gtsam/slam/BetweenFactor.h>
#include <iostream>
using namespace std;
using namespace gtsam;
int main() {
cout << "Graduated Non-Convexity Example\n";
NonlinearFactorGraph graph;
// Add a prior to the first point, set to the origin
auto priorNoise = noiseModel::Isotropic::Sigma(3, 0.1);
graph.addPrior(1, Pose2(0.0, 0.0, 0.0), priorNoise);
// Add additional factors, noise models must be Gaussian
Pose2 x1(1.0, 0.0, 0.1);
graph.emplace_shared<BetweenFactor<Pose2>>(1, 2, x1, noiseModel::Isotropic::Sigma(3, 0.2));
Pose2 x2(0.0, 1.0, 0.1);
graph.emplace_shared<BetweenFactor<Pose2>>(2, 3, x2, noiseModel::Isotropic::Sigma(3, 0.4));
// Initial estimates
Values initial;
initial.insert(1, Pose2(0.2, 0.5, -0.1));
initial.insert(2, Pose2(0.8, 0.3, 0.1));
initial.insert(3, Pose2(0.8, 0.2, 0.3));
// Set options for the non-minimal solver
LevenbergMarquardtParams lmParams;
lmParams.setMaxIterations(1000);
lmParams.setRelativeErrorTol(1e-5);
// Set GNC-specific options
GncParams<LevenbergMarquardtParams> gncParams(lmParams);
gncParams.setLossType(GncLossType::TLS);
// Optimize the graph and print results
GncOptimizer<GncParams<LevenbergMarquardtParams>> optimizer(graph, initial, gncParams);
Values result = optimizer.optimize();
result.print("Final Result:");
return 0;
}