99 lines
3.4 KiB
C++
99 lines
3.4 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 Pose2SLAMExample_g2o.cpp
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* @brief A 2D Pose SLAM example that reads input from g2o, converts it to a factor graph and does the
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* optimization. Output is written on a file, in g2o format
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* Syntax for the script is ./Pose2SLAMExample_g2o input.g2o output.g2o
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* @date May 15, 2014
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* @author Luca Carlone
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*/
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#include <gtsam/slam/dataset.h>
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#include <gtsam/slam/PriorFactor.h>
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#include <gtsam/nonlinear/GaussNewtonOptimizer.h>
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#include <fstream>
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using namespace std;
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using namespace gtsam;
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// HOWTO: ./Pose2SLAMExample_g2o inputFile outputFile (maxIterations) (tukey/huber)
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int main(const int argc, const char *argv[]) {
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string kernelType = "none";
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int maxIterations = 100; // default
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string g2oFile = findExampleDataFile("noisyToyGraph.txt"); // default
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// Parse user's inputs
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if (argc > 1){
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g2oFile = argv[1]; // input dataset filename
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// outputFile = g2oFile = argv[2]; // done later
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}
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if (argc > 3){
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maxIterations = atoi(argv[3]); // user can specify either tukey or huber
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}
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if (argc > 4){
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kernelType = argv[4]; // user can specify either tukey or huber
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}
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// reading file and creating factor graph
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NonlinearFactorGraph::shared_ptr graph;
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Values::shared_ptr initial;
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bool is3D = false;
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if(kernelType.compare("none") == 0){
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boost::tie(graph, initial) = readG2o(g2oFile,is3D);
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}
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if(kernelType.compare("huber") == 0){
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std::cout << "Using robust kernel: huber " << std::endl;
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boost::tie(graph, initial) = readG2o(g2oFile,is3D, KernelFunctionTypeHUBER);
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}
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if(kernelType.compare("tukey") == 0){
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std::cout << "Using robust kernel: tukey " << std::endl;
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boost::tie(graph, initial) = readG2o(g2oFile,is3D, KernelFunctionTypeTUKEY);
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}
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// Add prior on the pose having index (key) = 0
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NonlinearFactorGraph graphWithPrior = *graph;
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noiseModel::Diagonal::shared_ptr priorModel = //
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noiseModel::Diagonal::Variances(Vector3(1e-6, 1e-6, 1e-8));
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graphWithPrior.add(PriorFactor<Pose2>(0, Pose2(), priorModel));
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std::cout << "Adding prior on pose 0 " << std::endl;
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GaussNewtonParams params;
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params.setVerbosity("TERMINATION");
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if (argc > 3) {
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params.maxIterations = maxIterations;
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std::cout << "User required to perform maximum " << params.maxIterations << " iterations "<< std::endl;
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}
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std::cout << "Optimizing the factor graph" << std::endl;
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GaussNewtonOptimizer optimizer(graphWithPrior, *initial, params);
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Values result = optimizer.optimize();
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std::cout << "Optimization complete" << std::endl;
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std::cout << "initial error=" <<graph->error(*initial)<< std::endl;
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std::cout << "final error=" <<graph->error(result)<< std::endl;
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if (argc < 3) {
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result.print("result");
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} else {
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const string outputFile = argv[2];
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std::cout << "Writing results to file: " << outputFile << std::endl;
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NonlinearFactorGraph::shared_ptr graphNoKernel;
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Values::shared_ptr initial2;
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boost::tie(graphNoKernel, initial2) = readG2o(g2oFile);
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writeG2o(*graphNoKernel, result, outputFile);
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std::cout << "done! " << std::endl;
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}
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return 0;
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}
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