sba_demo.cpp

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// g2o - General Graph Optimization
// Copyright (C) 2011 H. Strasdat
// 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
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// TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
// PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
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#include <Eigen/StdVector>

#include <unordered_set>

#include <iostream>
#include <stdint.h>

#include "g2o/config.h"
#include "g2o/core/sparse_optimizer.h"
#include "g2o/core/block_solver.h"
#include "g2o/core/solver.h"
#include "g2o/core/robust_kernel_impl.h"
#include "g2o/core/optimization_algorithm_levenberg.h"
#include "g2o/solvers/dense/linear_solver_dense.h"
#include "g2o/types/icp/types_icp.h"
#include "g2o/solvers/structure_only/structure_only_solver.h"

#if defined G2O_HAVE_CHOLMOD
#include "g2o/solvers/cholmod/linear_solver_cholmod.h"
#elif defined G2O_HAVE_CSPARSE
#include "g2o/solvers/csparse/linear_solver_csparse.h"
#endif

using namespace Eigen;
using namespace std;


class Sample
{
public:
  static int uniform(int from, int to);
  static double uniform();
  static double gaussian(double sigma);
};

static double uniform_rand(double lowerBndr, double upperBndr)
{
  return lowerBndr + ((double) std::rand() / (RAND_MAX + 1.0)) * (upperBndr - lowerBndr);
}

static double gauss_rand(double mean, double sigma)
{
  double x, y, r2;
  do {
    x = -1.0 + 2.0 * uniform_rand(0.0, 1.0);
    y = -1.0 + 2.0 * uniform_rand(0.0, 1.0);
    r2 = x * x + y * y;
  } while (r2 > 1.0 || r2 == 0.0);
  return mean + sigma * y * std::sqrt(-2.0 * log(r2) / r2);
}

int Sample::uniform(int from, int to)
{
  return static_cast<int>(uniform_rand(from, to));
}

double Sample::uniform()
{
  return uniform_rand(0., 1.);
}

double Sample::gaussian(double sigma)
{
  return gauss_rand(0., sigma);
}

int main(int argc, const char* argv[])
{
  if (argc<2)
  {
    cout << endl;
    cout << "Please type: " << endl;
    cout << "ba_demo [PIXEL_NOISE] [OUTLIER RATIO] [ROBUST_KERNEL] [STRUCTURE_ONLY] [DENSE]" << endl;
    cout << endl;
    cout << "PIXEL_NOISE: noise in image space (E.g.: 1)" << endl;
    cout << "OUTLIER_RATIO: probability of spuroius observation  (default: 0.0)" << endl;
    cout << "ROBUST_KERNEL: use robust kernel (0 or 1; default: 0==false)" << endl;
    cout << "STRUCTURE_ONLY: performe structure-only BA to get better point initializations (0 or 1; default: 0==false)" << endl;
    cout << "DENSE: Use dense solver (0 or 1; default: 0==false)" << endl;
    cout << endl;
    cout << "Note, if OUTLIER_RATIO is above 0, ROBUST_KERNEL should be set to 1==true." << endl;
    cout << endl;
    exit(0);
  }

  double PIXEL_NOISE = atof(argv[1]);

  double OUTLIER_RATIO = 0.0;

  if (argc>2)
  {
    OUTLIER_RATIO = atof(argv[2]);
  }

  bool ROBUST_KERNEL = false;
  if (argc>3)
  {
    ROBUST_KERNEL = atoi(argv[3]) != 0;
  }
  bool STRUCTURE_ONLY = false;
  if (argc>4)
  {
    STRUCTURE_ONLY = atoi(argv[4]) != 0;
  }

  bool DENSE = false;
  if (argc>5)
  {
    DENSE = atoi(argv[5]) != 0;
  }

  cout << "PIXEL_NOISE: " <<  PIXEL_NOISE << endl;
  cout << "OUTLIER_RATIO: " << OUTLIER_RATIO<<  endl;
  cout << "ROBUST_KERNEL: " << ROBUST_KERNEL << endl;
  cout << "STRUCTURE_ONLY: " << STRUCTURE_ONLY<< endl;
  cout << "DENSE: "<<  DENSE << endl;



  g2o::SparseOptimizer optimizer;
  optimizer.setVerbose(false);
  g2o::BlockSolver_6_3::LinearSolverType * linearSolver;
  if (DENSE)
  {
        linearSolver= new g2o::LinearSolverDense<g2o::BlockSolver_6_3::PoseMatrixType>();
                cerr << "Using DENSE" << endl;
  }
  else
  {
#ifdef G2O_HAVE_CHOLMOD
        cerr << "Using CHOLMOD" << endl;
    linearSolver = new g2o::LinearSolverCholmod<g2o::BlockSolver_6_3::PoseMatrixType>();
#elif defined G2O_HAVE_CSPARSE
    linearSolver = new g2o::LinearSolverCSparse<g2o::BlockSolver_6_3::PoseMatrixType>();
        cerr << "Using CSPARSE" << endl;
#else
#error neither CSparse nor Cholmod are available
#endif
  }


  g2o::BlockSolver_6_3 * solver_ptr
      = new g2o::BlockSolver_6_3(linearSolver);

  g2o::OptimizationAlgorithmLevenberg* solver = new g2o::OptimizationAlgorithmLevenberg(solver_ptr);

  optimizer.setAlgorithm(solver);

  // set up 500 points
  vector<Vector3d> true_points;
  for (size_t i=0;i<500; ++i)
  {
    true_points.push_back(Vector3d((Sample::uniform()-0.5)*3,
                                   Sample::uniform()-0.5,
                                   Sample::uniform()+10));
  }


  Vector2d focal_length(500,500); // pixels
  Vector2d principal_point(320,240); // 640x480 image
  double baseline = 0.075;      // 7.5 cm baseline


  vector<Eigen::Isometry3d,
      aligned_allocator<Eigen::Isometry3d> > true_poses;

  // set up camera params
  g2o::VertexSCam::setKcam(focal_length[0],focal_length[1],
                           principal_point[0],principal_point[1],
                           baseline);
  
  // set up 5 vertices, first 2 fixed
  int vertex_id = 0;
  for (size_t i=0; i<5; ++i)
  {


    Vector3d trans(i*0.04-1.,0,0);

    Eigen:: Quaterniond q;
    q.setIdentity();
    Eigen::Isometry3d pose;
    pose = q;
    pose.translation() = trans;


    g2o::VertexSCam * v_se3
        = new g2o::VertexSCam();

    v_se3->setId(vertex_id);
    v_se3->setEstimate(pose);
    v_se3->setAll();            // set aux transforms

    if (i<2)
      v_se3->setFixed(true);
    
    optimizer.addVertex(v_se3);
    true_poses.push_back(pose);
    vertex_id++;
  }

  int point_id=vertex_id;
  int point_num = 0;
  double sum_diff2 = 0;

  cout << endl;
  unordered_map<int,int> pointid_2_trueid;
  unordered_set<int> inliers;

  // add point projections to this vertex
  for (size_t i=0; i<true_points.size(); ++i)
  {
    g2o::VertexSBAPointXYZ * v_p
        = new g2o::VertexSBAPointXYZ();


    v_p->setId(point_id);
    v_p->setMarginalized(true);
    v_p->setEstimate(true_points.at(i)
        + Vector3d(Sample::gaussian(1),
                   Sample::gaussian(1),
                   Sample::gaussian(1)));

    int num_obs = 0;

    for (size_t j=0; j<true_poses.size(); ++j)
    {
      Vector3d z;
      dynamic_cast<g2o::VertexSCam*>
        (optimizer.vertices().find(j)->second)
        ->mapPoint(z,true_points.at(i));

      if (z[0]>=0 && z[1]>=0 && z[0]<640 && z[1]<480)
      {
        ++num_obs;
      }
    }

    if (num_obs>=2)
    {
      optimizer.addVertex(v_p);

      bool inlier = true;
      for (size_t j=0; j<true_poses.size(); ++j)
      {
        Vector3d z;
        dynamic_cast<g2o::VertexSCam*>
          (optimizer.vertices().find(j)->second)
          ->mapPoint(z,true_points.at(i));

        if (z[0]>=0 && z[1]>=0 && z[0]<640 && z[1]<480)
        {
          double sam = Sample::uniform();
          if (sam<OUTLIER_RATIO)
          {
            z = Vector3d(Sample::uniform(64,640),
                         Sample::uniform(0,480),
                         Sample::uniform(0,64)); // disparity
            z(2) = z(0) - z(2); // px' now

            inlier= false;
          }

          z += Vector3d(Sample::gaussian(PIXEL_NOISE),
                        Sample::gaussian(PIXEL_NOISE),
                        Sample::gaussian(PIXEL_NOISE/16.0));

          g2o::Edge_XYZ_VSC * e
              = new g2o::Edge_XYZ_VSC();


          e->vertices()[0]
              = dynamic_cast<g2o::OptimizableGraph::Vertex*>(v_p);

          e->vertices()[1]
              = dynamic_cast<g2o::OptimizableGraph::Vertex*>
              (optimizer.vertices().find(j)->second);

          e->setMeasurement(z);
          //e->inverseMeasurement() = -z;
          e->information() = Matrix3d::Identity();

          if (ROBUST_KERNEL) {
            g2o::RobustKernelHuber* rk = new g2o::RobustKernelHuber;
            e->setRobustKernel(rk);
          }

          optimizer.addEdge(e);


        }

      }

      if (inlier)
      {
        inliers.insert(point_id);
        Vector3d diff = v_p->estimate() - true_points[i];

        sum_diff2 += diff.dot(diff);
      }
     // else
     //   cout << "Point: " << point_id <<  "has at least one spurious observation" <<endl;

      pointid_2_trueid.insert(make_pair(point_id,i));

      ++point_id;
      ++point_num;
    }

  }

  cout << endl;
  optimizer.initializeOptimization();

  optimizer.setVerbose(true);

  if (STRUCTURE_ONLY)
  {
    cout << "Performing structure-only BA:"   << endl;
    g2o::StructureOnlySolver<3> structure_only_ba;
    g2o::OptimizableGraph::VertexContainer points;
    for (g2o::OptimizableGraph::VertexIDMap::const_iterator it = optimizer.vertices().begin(); it != optimizer.vertices().end(); ++it) {
      g2o::OptimizableGraph::Vertex* v = static_cast<g2o::OptimizableGraph::Vertex*>(it->second);
      if (v->dimension() == 3)
        points.push_back(v);
    }

    structure_only_ba.calc(points, 10);
  }

    cout << endl;
  cout << "Performing full BA:" << endl;
  optimizer.optimize(10);

  cout << endl;
  cout << "Point error before optimisation (inliers only): " << sqrt(sum_diff2/point_num) << endl;


  point_num = 0;
  sum_diff2 = 0;


  for (unordered_map<int,int>::iterator it=pointid_2_trueid.begin();
       it!=pointid_2_trueid.end(); ++it)
  {

    g2o::HyperGraph::VertexIDMap::iterator v_it
        = optimizer.vertices().find(it->first);

    if (v_it==optimizer.vertices().end())
    {
      cerr << "Vertex " << it->first << " not in graph!" << endl;
      exit(-1);
    }

    g2o::VertexSBAPointXYZ * v_p
        = dynamic_cast< g2o::VertexSBAPointXYZ * > (v_it->second);

    if (v_p==0)
    {
      cerr << "Vertex " << it->first << "is not a PointXYZ!" << endl;
      exit(-1);
    }

    Vector3d diff = v_p->estimate()-true_points[it->second];

    if (inliers.find(it->first)==inliers.end())
      continue;

    sum_diff2 += diff.dot(diff);

    ++point_num;
  }

  cout << "Point error after optimisation (inliers only): " << sqrt(sum_diff2/point_num) << endl;
  cout << endl;

}