static_target.cpp

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// g2o - General Graph Optimization
// Copyright (C) 2011 R. Kuemmerle, G. Grisetti, H. Strasdat, W. Burgard
// All rights reserved.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are
// met:
//
// * Redistributions of source code must retain the above copyright notice,
//   this list of conditions and the following disclaimer.
// * Redistributions in binary form must reproduce the above copyright
//   notice, this list of conditions and the following disclaimer in the
//   documentation and/or other materials provided with the distribution.
//
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS
// IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED
// TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
// PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
// HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
// TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
// PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
// LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
// NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
// SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
// This example consists of a single static target which sits in one
// place and does not move; in effect it has a "GPS" which measures
// its position
 
#include <g2o/core/block_solver.h>
#include <g2o/core/optimization_algorithm_gauss_newton.h>
#include <g2o/core/solver.h>
#include <g2o/core/sparse_optimizer.h>
#include <g2o/solvers/eigen/linear_solver_eigen.h>
#include <g2o/stuff/sampler.h>
 
#include <iostream>
 
#include "targetTypes3D.hpp"
 
using namespace Eigen;
using namespace std;
using namespace g2o;
 
int main() {
  // Set up the optimiser
  SparseOptimizer optimizer;
  optimizer.setVerbose(false);
 
  // Create the block solver - the dimensions are specified because
  // 3D observations marginalise to a 3D estimate
  typedef BlockSolver<BlockSolverTraits<3, 3>> BlockSolver_3_3;
  OptimizationAlgorithmGaussNewton* solver =
      new OptimizationAlgorithmGaussNewton(std::make_unique<BlockSolver_3_3>(
          std::make_unique<
              LinearSolverEigen<BlockSolver_3_3::PoseMatrixType>>()));
 
  optimizer.setAlgorithm(solver);
 
  // Sample the actual location of the target
  Vector3d truePoint(sampleUniform(-500, 500), sampleUniform(-500, 500),
                     sampleUniform(-500, 500));
 
  // Construct vertex which corresponds to the actual point of the target
  VertexPosition3D* position = new VertexPosition3D();
  position->setId(0);
  optimizer.addVertex(position);
 
  // Now generate some noise corrupted measurements; for simplicity
  // these are uniformly distributed about the true target. These are
  // modelled as a unary edge because they do not like to, say,
  // another node in the map.
  int numMeasurements = 10;
  double noiseLimit = sqrt(12.);
  double noiseSigma = noiseLimit * noiseLimit / 12.0;
 
  for (int i = 0; i < numMeasurements; i++) {
    Vector3d measurement =
        truePoint + Vector3d(sampleUniform(-0.5, 0.5) * noiseLimit,
                             sampleUniform(-0.5, 0.5) * noiseLimit,
                             sampleUniform(-0.5, 0.5) * noiseLimit);
    GPSObservationPosition3DEdge* goe = new GPSObservationPosition3DEdge();
    goe->setVertex(0, position);
    goe->setMeasurement(measurement);
    goe->setInformation(Matrix3d::Identity() / noiseSigma);
    optimizer.addEdge(goe);
  }
 
  // Configure and set things going
  optimizer.initializeOptimization();
  optimizer.setVerbose(true);
  optimizer.optimize(5);
 
  cout << "truePoint=\n" << truePoint << endl;
  cerr << "computed estimate=\n"
       << dynamic_cast<VertexPosition3D*>(optimizer.vertices().find(0)->second)
              ->estimate()
       << endl;
 
  // position->setMarginalized(true);
 
  SparseBlockMatrix<MatrixXd> spinv;
  bool state = optimizer.computeMarginals(spinv, position);
  if (state) {
    cout << "covariance\n" << spinv << endl;
    cout << spinv.block(0, 0) << endl;
  }
 
  return 0;
}