constant_velocity_target.cpp File Reference

#include <Eigen/StdVector>
#include <iostream>
#include <stdint.h>
#include <g2o/core/sparse_optimizer.h>
#include <g2o/core/block_solver.h>
#include <g2o/core/solver.h>
#include <g2o/core/optimization_algorithm_gauss_newton.h>
#include <g2o/solvers/cholmod/linear_solver_cholmod.h>
#include <g2o/solvers/pcg/linear_solver_pcg.h>
#include <g2o/stuff/sampler.h>
#include "targetTypes6D.hpp"
#include "continuous_to_discrete.h"

Include dependency graph for constant_velocity_target.cpp:

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Functions
int	main ()

Function Documentation

int main

(

)

Definition at line 24 of file constant_velocity_target.cpp.

References g2o::OptimizableGraph::addEdge(), g2o::OptimizableGraph::addVertex(), g2o::HyperGraph::edges(), TargetOdometry3DEdge::initialEstimate(), g2o::SparseOptimizer::initializeOptimization(), g2o::SparseOptimizer::optimize(), g2o::sampleGaussian(), g2o::SparseOptimizer::setAlgorithm(), g2o::BaseVertex< D, T >::setEstimate(), g2o::OptimizableGraph::Vertex::setId(), g2o::OptimizableGraph::Vertex::setMarginalized(), g2o::BaseEdge< D, E >::setMeasurement(), g2o::SparseOptimizer::setVerbose(), g2o::HyperGraph::Edge::setVertex(), and g2o::HyperGraph::vertices().

{
  // Set up the parameters of the simulation
  int numberOfTimeSteps = 1000;
  const double processNoiseSigma = 1;
  const double accelerometerNoiseSigma = 1;
  const double gpsNoiseSigma = 1;
  const double dt = 1;  

  // Set up the optimiser and block solver
  SparseOptimizer optimizer;
  optimizer.setVerbose(false);

  typedef BlockSolver< BlockSolverTraits<6, 6> > BlockSolver;
  BlockSolver::LinearSolverType * linearSolver
      = new LinearSolverCholmod<BlockSolver::PoseMatrixType>();
  BlockSolver* blockSolver = new BlockSolver(linearSolver);
  OptimizationAlgorithm* optimizationAlgorithm = new OptimizationAlgorithmGaussNewton(blockSolver);
  optimizer.setAlgorithm(optimizationAlgorithm);

  // Sample the start location of the target
  Vector6d state;
  state.setZero();
  for (int k = 0; k < 3; k++)
    {
      state[k] = 1000 * sampleGaussian();
    }
  
  // Construct the first vertex; this corresponds to the initial
  // condition and register it with the optimiser
  VertexPositionVelocity3D* stateNode = new VertexPositionVelocity3D();
  stateNode->setEstimate(state);
  stateNode->setId(0);
  optimizer.addVertex(stateNode);

  // Set up last estimate
  VertexPositionVelocity3D* lastStateNode = stateNode;

  // Iterate over the simulation steps
  for (int k = 1; k <= numberOfTimeSteps; ++k)
    {
      // Simulate the next step; update the state and compute the observation
      Vector3d processNoise(processNoiseSigma*sampleGaussian(),
                            processNoiseSigma*sampleGaussian(),
                            processNoiseSigma*sampleGaussian());

      for (int m = 0; m < 3; m++)
        {
          state[m] += dt * (state[m+3] + 0.5 * dt * processNoise[m]);
        }

      for (int m = 0; m < 3; m++)
        {
          state[m+3] += dt * processNoise[m];
        }

      // Construct the accelerometer measurement
      Vector3d accelerometerMeasurement;
      for (int m = 0; m < 3; m++)
        {
          accelerometerMeasurement[m] = processNoise[m] + accelerometerNoiseSigma * sampleGaussian();
        }

      // Construct the GPS observation
      Vector3d gpsMeasurement;     
      for (int m = 0; m < 3; m++)
        {
          gpsMeasurement[m] = state[m] + gpsNoiseSigma * sampleGaussian();
        }

      // Construct vertex which corresponds to the current state of the target
      VertexPositionVelocity3D* stateNode = new VertexPositionVelocity3D();
      
      stateNode->setId(k);
      stateNode->setMarginalized(false);
      optimizer.addVertex(stateNode);

      TargetOdometry3DEdge* toe = new TargetOdometry3DEdge(dt, accelerometerNoiseSigma);
      toe->setVertex(0, lastStateNode);
      toe->setVertex(1, stateNode);
      VertexPositionVelocity3D* vPrev= dynamic_cast<VertexPositionVelocity3D*>(lastStateNode);
      VertexPositionVelocity3D* vCurr= dynamic_cast<VertexPositionVelocity3D*>(stateNode);
      toe->setMeasurement(accelerometerMeasurement);
      optimizer.addEdge(toe);
      
      // compute the initial guess via the odometry
      g2o::OptimizableGraph::VertexSet vPrevSet;
      vPrevSet.insert(vPrev);
      toe->initialEstimate(vPrevSet,vCurr);

      lastStateNode = stateNode;

      // Add the GPS observation
      GPSObservationEdgePositionVelocity3D* goe = new GPSObservationEdgePositionVelocity3D(gpsMeasurement, gpsNoiseSigma);
      goe->setVertex(0, stateNode);
      optimizer.addEdge(goe);
    }

  // Configure and set things going
  optimizer.initializeOptimization();
  optimizer.setVerbose(true);
  optimizer.optimize(5);
  cerr << "number of vertices:" << optimizer.vertices().size() << endl;
  cerr << "number of edges:" << optimizer.edges().size() << endl;

  // Print the results

  cout << "state=\n" << state << endl;

#if 0
  for (int k = 0; k < numberOfTimeSteps; k++)
    {
      cout << "computed estimate " << k << "\n"
           << dynamic_cast<VertexPositionVelocity3D*>(optimizer.vertices().find(k)->second)->estimate() << endl;
       }
#endif

  Vector6d v1 = dynamic_cast<VertexPositionVelocity3D*>(optimizer.vertices().find((std::max)(numberOfTimeSteps-2,0))->second)->estimate();
  Vector6d v2 = dynamic_cast<VertexPositionVelocity3D*>(optimizer.vertices().find((std::max)(numberOfTimeSteps-1,0))->second)->estimate();
  cout << "v1=\n" << v1 << endl;
  cout << "v2=\n" << v2 << endl;
  cout << "delta state=\n" << v2-v1 << endl;
}