/* ---------------------------------------------------------------------------- * 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 Pose2SLAMExample.cpp * @brief A 2D Pose SLAM example that reads input from g2o and uses robust kernels in optimization * @date May 15, 2014 * @author Luca Carlone */ #include #include #include #include #include #include #include #include #include #include #include using namespace std; using namespace gtsam; #define LINESIZE 81920 int main(const int argc, const char *argv[]){ if (argc < 2) std::cout << "Please specify input file (in g2o format) and output file" << std::endl; const string g2oFile = argv[1]; NonlinearFactorGraph graph; Values initial; readG2o(g2oFile, graph, initial); // otherwise GTSAM cannot solve the problem NonlinearFactorGraph graphWithPrior = graph; noiseModel::Diagonal::shared_ptr priorModel = noiseModel::Diagonal::Variances((Vector(3) << 0.01, 0.01, 0.001)); graphWithPrior.add(PriorFactor(0, Pose2(), priorModel)); // Create the optimizer ... std::cout << "Optimizing the factor graph" << std::endl; GaussNewtonOptimizer optimizer(graphWithPrior, initial); // , parameters); // ... and optimize Values result = optimizer.optimize(); std::cout << "Optimization complete" << std::endl; const string outputFile = argv[2]; std::cout << "Writing results to file: " << outputFile << std::endl; noiseModel::Diagonal::shared_ptr model = noiseModel::Diagonal::Sigmas((Vector(3) << 0.0, 0.0, 0.0)); writeG2o(graph, result, model, outputFile); std::cout << "done! " << std::endl; return 0; }