コード例 #1
0
Eigen::Matrix3f Homography::calcHomography( const Eigen::Isometry3f &camA2plane, const Eigen::Isometry3f &camB2plane ) const
{
    // calculate the plane normal (z-vector) from the point of camB
    Eigen::Vector3f n = camB2plane.rotation().transpose() * -Eigen::Vector3f::UnitZ();

    // calculate the distance from camB to plane
    float d = camB2plane.inverse().translation().dot( n );

    // get transform from camB to camA
    Eigen::Isometry3f camBtoCamA = camA2plane.inverse() * camB2plane;

    // get the homography
    return calcHomography( camBtoCamA, n, d );
}
コード例 #2
0
void KinectDatasetReader::MoveGroundTruthsToOrigin()
{
    Eigen::Isometry3f origin = mGroundTruth[0].LocalTransform;
    origin = origin.inverse();
    
    for(size_t i = 0; i < mGroundTruth.size(); i++)
        mGroundTruth[i].LocalTransform = origin * mGroundTruth[i].LocalTransform;
}
コード例 #3
0
ファイル: Utils.cpp プロジェクト: Gastd/oh-distro
bool Utils::
composeViewMatrix(Eigen::Projective3f& oMatrix, const Eigen::Matrix3f& iCalib,
                  const Eigen::Isometry3f& iPose, const bool iIsOrthographic) {
  Eigen::Projective3f calib = Eigen::Projective3f::Identity();
  calib.matrix().col(2).swap(calib.matrix().col(3));
  calib.matrix().topLeftCorner<2,3>() = iCalib.matrix().topRows<2>();
  calib.matrix().bottomLeftCorner<1,3>() = iCalib.matrix().bottomRows<1>();
  if (iIsOrthographic) calib.matrix().col(2).swap(calib.matrix().col(3));
  oMatrix = calib*iPose.inverse();
  return true;
}
コード例 #4
0
ファイル: pwn_g2o.cpp プロジェクト: 9578577/g2o_frontend
int
 main(int argc, char** argv){
  string dirname;
  string graphFilename;
  float numThreads;

  int ng_step;
  int ng_minPoints;
  int ng_imageRadius;
  float ng_worldRadius;
  float ng_maxCurvature;
  
  float al_inlierDistance;
  float al_inlierCurvatureRatio;
  float al_inlierNormalAngle;
  float al_inlierMaxChi2;
  float al_scale;
  float al_flatCurvatureThreshold;
  float al_outerIterations;
  float al_nonLinearIterations;
  float al_linearIterations;
  float al_minInliers;
  float al_lambda;
  float al_debug;

  PointWithNormalStatistcsGenerator normalGenerator;
  PointWithNormalAligner aligner;

  g2o::CommandArgs arg;
  arg.param("ng_step",ng_step,normalGenerator.step(),"compute a normal each x pixels") ;
  arg.param("ng_minPoints",ng_minPoints,normalGenerator.minPoints(),"minimum number of points in a region to compute the normal");
  arg.param("ng_imageRadius",ng_imageRadius,normalGenerator.imageRadius(), "radius of a ball in the works where to compute the normal for a pixel");
  arg.param("ng_worldRadius",ng_worldRadius,  normalGenerator.worldRadius(), "radius of a ball in the works where to compute the normal for a pixel");
  arg.param("ng_maxCurvature",ng_maxCurvature, normalGenerator.maxCurvature(), "above this threshold the normal is not computed");
  
  arg.param("al_inlierDistance", al_inlierDistance, aligner.inlierDistanceThreshold(),  "max metric distance between two points to regard them as iniliers");
  arg.param("al_inlierCurvatureRatio", al_inlierCurvatureRatio, aligner.inlierCurvatureRatioThreshold(), "max metric distance between two points to regard them as iniliers");
  arg.param("al_inlierNormalAngle", al_inlierNormalAngle, aligner.inlierNormalAngularThreshold(), "max metric distance between two points to regard them as iniliers");
  arg.param("al_inlierMaxChi2", al_inlierMaxChi2, aligner.inlierMaxChi2(), "max metric distance between two points to regard them as iniliers");
  arg.param("al_minInliers", al_minInliers, aligner.minInliers(), "minimum numver of inliers to do the matching");
  arg.param("al_scale", al_scale, aligner.scale(), "scale of the range image for the alignment");
  arg.param("al_flatCurvatureThreshold", al_flatCurvatureThreshold, aligner.flatCurvatureThreshold(), "curvature above which the patches are not considered planar");
  arg.param("al_outerIterations", al_outerIterations, aligner.outerIterations(), "outer interations (incl. data association)");
  arg.param("al_linearIterations", al_linearIterations, aligner.linearIterations(), "linear iterations for each outer one (uses R,t)");
  arg.param("al_nonLinearIterations", al_nonLinearIterations, aligner.nonLinearIterations(), "nonlinear iterations for each outer one (uses q,t)");
  arg.param("al_lambda", al_lambda, aligner.lambda(), "damping factor for the transformation update, the higher the smaller the step");
  arg.param("al_debug", al_debug, aligner.debug(), "prints lots of stuff");
  arg.param("numThreads", numThreads, 1, "numver of threads for openmp");
  

  if (numThreads<1)
    numThreads = 1;
  
  std::vector<string> fileNames;

  arg.paramLeftOver("dirname", dirname, "", "", true);
  arg.paramLeftOver("graph-file", graphFilename, "out.g2o", "graph-file", true);
  arg.parseArgs(argc, argv);
  cerr << "dirname " << dirname << endl;

  std::vector<string> filenames;
  std::set<string> filenamesset = readDir(dirname);
  for(set<string>::const_iterator it =filenamesset.begin(); it!=filenamesset.end(); it++) {
    filenames.push_back(*it);
  }
  
  normalGenerator.setStep(ng_step);
  normalGenerator.setMinPoints(ng_minPoints);
  normalGenerator.setImageRadius(ng_imageRadius);
  normalGenerator.setWorldRadius(ng_worldRadius);
  normalGenerator.setMaxCurvature(ng_maxCurvature);
#ifdef _PWN_USE_OPENMP_
  normalGenerator.setNumThreads(numThreads);
#endif //_PWN_USE_OPENMP_

  aligner.setInlierDistanceThreshold(al_inlierDistance);
  aligner.setInlierCurvatureRatioThreshold(al_inlierCurvatureRatio);
  aligner.setInlierNormalAngularThreshold(al_inlierNormalAngle);
  aligner.setInlierMaxChi2(al_inlierMaxChi2);
  aligner.setMinInliers(al_minInliers);
  aligner.setScale(al_scale);
  aligner.setFlatCurvatureThreshold(al_flatCurvatureThreshold);
  aligner.setOuterIterations(al_outerIterations);
  aligner.setLinearIterations(al_linearIterations);
  aligner.setNonLinearIterations(al_nonLinearIterations);
  aligner.setLambda(al_lambda);
  aligner.setDebug(al_debug);
#ifdef _PWN_USE_OPENMP_
  aligner.setNumThreads(numThreads);
#endif //_PWN_USE_OPENMP_


  Frame* referenceFrame= 0;

  Eigen::Matrix3f cameraMatrix;
  cameraMatrix << 
    525.0f, 0.0f, 319.5f,
    0.0f, 525.0f, 239.5f,
    0.0f, 0.0f, 1.0f;

  ostringstream os(graphFilename.c_str());
  

  cerr << "there are " << filenames.size() << " files  in the pool" << endl; 
  Isometry3f trajectory;
  trajectory.setIdentity();
  int previousIndex=-1;
  int graphNum=0;
  int nFrames = 0;
  string baseFilename = graphFilename.substr( 0, graphFilename.find_last_of( '.' ) );
  for (size_t i=0; i<filenames.size(); i++){
    cerr << endl << endl << endl;
    cerr << ">>>>>>>>>>>>>>>>>>>>>>>> PROCESSING " << filenames[i] << " <<<<<<<<<<<<<<<<<<<<" <<  endl;
    Frame* currentFrame= new Frame();
    if(!currentFrame->load(filenames[i])) {
      cerr << "failure in loading image: " << filenames[i] << ", skipping" << endl;
      delete currentFrame;
      continue;
    }
    nFrames ++;
    if (! referenceFrame ){
      os << "PARAMS_CAMERACALIB 0 0 0 0 0 0 0 1 525 525 319.5 239.5"<< endl;
      trajectory.setIdentity();
    }
    {
      // write the vertex frame
      Vector6f x = t2v(trajectory);
      Vector3f t = x.head<3>();
      Vector3f mq = x.tail<3>();
      float w = mq.squaredNorm();
      if (w>1){
	mq.setZero();
	w = 1.0f;
      } else {
	w = sqrt(1-w);
      }
      
      os << "VERTEX_SE3:QUAT " << i << " " << t.transpose() << " " << mq.transpose() << " " << w << endl;
      os << "DEPTH_IMAGE_DATA 0 " << filenames[i] << " 0 0 " << endl;
    }
    
    currentFrame->computeStats(normalGenerator,cameraMatrix);
    if (referenceFrame) {
      referenceFrame->setAligner(aligner, true);
      currentFrame->setAligner(aligner, false);

      Matrix6f omega;
      Vector6f mean;
      float tratio;
      float rratio;
      aligner.setImageSize(currentFrame->depthImage.rows(), currentFrame->depthImage.cols());
      Eigen::Isometry3f X;
      X.setIdentity();
      double ostart = get_time();
      float error;
      int result = aligner.align(error, X, mean, omega, tratio, rratio);
      cerr << "inliers=" << result << " error/inliers: " << error/result << endl;
      cerr << "localTransform : " << endl;
      cerr << X.inverse().matrix() << endl;
      trajectory=trajectory*X;
      cerr << "globaltransform: " << endl;
      cerr << trajectory.matrix() << endl;
      double oend = get_time();
      cerr << "alignment took: " << oend-ostart << " sec." << endl;
      cerr << "aligner scaled image size: " << aligner.scaledImageRows() << " " << aligner.scaledImageCols() << endl;
      
      if(rratio < 100 && tratio < 100) {
	// write the edge frame
	Vector6f x = mean;
	Vector3f t = x.head<3>();
	Vector3f mq = x.tail<3>();
	float w = mq.squaredNorm();
	if (w>1){
	  mq.setZero();
	  w = 1.0f;
	} else {
	  w = sqrt(1-w);
	}
	
	os << "EDGE_SE3:QUAT " << previousIndex << " " << i << " ";
	os << t.transpose() << " " << mq.transpose() << " " << w <<  " ";
	for (int r=0; r<6; r++){
	  for (int c=r; c<6; c++){
	    os << omega(r,c) << " ";
	  }
	} 
	os << endl;
      } else {
	if (nFrames >10) {
	  char buf[1024];
	  sprintf(buf, "%s-%03d.g2o", baseFilename.c_str(), graphNum);	
	  ofstream gs(buf);
	  gs << os.str();
	  gs.close();
	}
	os.str("");
	os.clear();
	if (referenceFrame)
	  delete referenceFrame;
	referenceFrame = 0;
	graphNum ++;
	nFrames = 0;
      }

    }
    previousIndex = i;
    if (referenceFrame)
      delete referenceFrame;
    referenceFrame = currentFrame;
  }

  char buf[1024];
  sprintf(buf, "%s-%03d.g2o", baseFilename.c_str(), graphNum);	
  cerr << "saving final frames, n: " << nFrames << " in file [" << buf << "]" << endl;
  cerr << "filesize:" << os.str().length() << endl; 
  ofstream gs(buf);
  gs << os.str();
  cout << os.str();
  gs.close();
  
}
コード例 #5
0
ファイル: ICPOdometry.cpp プロジェクト: ahundt/ICPCUDA
void ICPOdometry::getIncrementalTransformation(Eigen::Vector3f & trans, Eigen::Matrix<float, 3, 3, Eigen::RowMajor> & rot, int threads, int blocks)
{
    iterations[0] = 10;
    iterations[1] = 5;
    iterations[2] = 4;

    Eigen::Matrix<float, 3, 3, Eigen::RowMajor> Rprev = rot;
    Eigen::Vector3f tprev = trans;

    Eigen::Matrix<float, 3, 3, Eigen::RowMajor> Rcurr = Rprev;
    Eigen::Vector3f tcurr = tprev;

    Eigen::Matrix<float, 3, 3, Eigen::RowMajor> Rprev_inv = Rprev.inverse();
    Mat33 & device_Rprev_inv = device_cast<Mat33>(Rprev_inv);
    float3& device_tprev = device_cast<float3>(tprev);

    cv::Mat resultRt = cv::Mat::eye(4, 4, CV_64FC1);

    for(int i = NUM_PYRS - 1; i >= 0; i--)
    {
        for(int j = 0; j < iterations[i]; j++)
        {
            Eigen::Matrix<float, 6, 6, Eigen::RowMajor> A_icp;
            Eigen::Matrix<float, 6, 1> b_icp;

            Mat33&  device_Rcurr = device_cast<Mat33> (Rcurr);
            float3& device_tcurr = device_cast<float3>(tcurr);

            DeviceArray2D<float>& vmap_curr = vmaps_curr_[i];
            DeviceArray2D<float>& nmap_curr = nmaps_curr_[i];

            DeviceArray2D<float>& vmap_g_prev = vmaps_g_prev_[i];
            DeviceArray2D<float>& nmap_g_prev = nmaps_g_prev_[i];

            float residual[2];

            icpStep(device_Rcurr,
                    device_tcurr,
                    vmap_curr,
                    nmap_curr,
                    device_Rprev_inv,
                    device_tprev,
                    intr(i),
                    vmap_g_prev,
                    nmap_g_prev,
                    distThres_,
                    angleThres_,
                    sumData,
                    outData,
                    A_icp.data(),
                    b_icp.data(),
                    &residual[0],
                    threads,
                    blocks);

            lastICPError = sqrt(residual[0]) / residual[1];
            lastICPCount = residual[1];

            Eigen::Matrix<double, 6, 1> result;
            Eigen::Matrix<double, 6, 6, Eigen::RowMajor> dA_icp = A_icp.cast<double>();
            Eigen::Matrix<double, 6, 1> db_icp = b_icp.cast<double>();

            lastA = dA_icp;
            lastb = db_icp;
            result = lastA.ldlt().solve(lastb);

            Eigen::Isometry3f incOdom;

            OdometryProvider::computeProjectiveMatrix(resultRt, result, incOdom);

            Eigen::Isometry3f currentT;
            currentT.setIdentity();
            currentT.rotate(Rprev);
            currentT.translation() = tprev;

            currentT = currentT * incOdom.inverse();

            tcurr = currentT.translation();
            Rcurr = currentT.rotation();
        }
    }

    trans = tcurr;
    rot = Rcurr;
}
コード例 #6
0
ファイル: aligner.cpp プロジェクト: 9578577/g2o_frontend
void Aligner::align() {
  struct timeval tvStart, tvEnd;
  gettimeofday(&tvStart,0);

  if (! _projector || !_linearizer || !_correspondenceGenerator){
    cerr << "I do nothing since you did not set all required algorithms" << endl;
    return;
  }
  // the current points are seen from the frame of the sensor
  _projector->setTransform(_sensorOffset);
  _projector->project(_correspondenceGenerator->currentIndexImage(),
		      _correspondenceGenerator->currentDepthImage(),
		      _currentScene->points());
  _T = _initialGuess;
  for(int i = 0; i < _outerIterations; i++) {
    //cout << "********************* Iteration " << i << " *********************" << endl;
    
    /************************************************************************
     *                         Correspondence Computation                   *
     ************************************************************************/
    //cout << "Computing correspondences...";
    
    // compute the indices of the current scene from the point of view of the sensor
    _projector->setTransform(_T*_sensorOffset);
    _projector->project(_correspondenceGenerator->referenceIndexImage(),
			_correspondenceGenerator->referenceDepthImage(),
			_referenceScene->points());
    
    // Correspondences computation.    
    _correspondenceGenerator->compute(*_referenceScene, *_currentScene, _T.inverse());

    //cout << " done." << endl;
    //cout << "# inliers found: " << _correspondenceGenerator->numCorrespondences() << endl;

    /************************************************************************
     *                            Alignment                                 *
     ************************************************************************/

    Eigen::Isometry3f invT = _T.inverse();
    for (int k = 0; k < _innerIterations; k++) {      
      invT.matrix().block<1, 4>(3, 0) << 0, 0, 0, 1;
      Matrix6f H;
      Vector6f b;

      _linearizer->setT(invT);
      _linearizer->update();
      H = _linearizer->H() + Matrix6f::Identity() * 10.0f;
      b = _linearizer->b();
      
      // add the priors
      for (size_t i=0; i<_priors.size(); i++){
	const SE3Prior* prior = _priors[i];
	Vector6f priorError = prior->error(invT);
	Matrix6f priorJacobian = prior->jacobian(invT);
	Matrix6f priorInformationRemapped = prior->errorInformation(invT);

	Matrix6f Hp = priorJacobian.transpose()*priorInformationRemapped*priorJacobian;
	Vector6f bp = priorJacobian.transpose()*priorInformationRemapped*priorError;

	H += Hp;
	b += bp;
      }


      Vector6f dx = H.ldlt().solve(-b);
      Eigen::Isometry3f dT = v2t(dx);
      invT = dT * invT;
    }
    _T = invT.inverse();
    _T.matrix().block<1, 4>(3, 0) << 0, 0, 0, 1;
  }

  gettimeofday(&tvEnd, 0);
  double tStart = tvStart.tv_sec*1000+tvStart.tv_usec*0.001;
  double tEnd = tvEnd.tv_sec*1000+tvEnd.tv_usec*0.001;
  _totalTime = tEnd - tStart;
  _error = _linearizer->error();
  _inliers = _linearizer->inliers();
}
コード例 #7
0
void PointWithNormalStatistcsGenerator::computeNormalsAndSVD(PointWithNormalVector& points, PointWithNormalSVDVector& svds, const Eigen::MatrixXi& indices, 
							     const Eigen::Matrix3f& cameraMatrix, const Eigen::Isometry3f& transform){
  _integralImage.compute(indices,points);
  int q=0;
  int outerStep = _numThreads * _step;
  PixelMapper pixelMapper;
  pixelMapper.setCameraMatrix(cameraMatrix);
  pixelMapper.setTransform(transform);
  Eigen::Isometry3f inverseTransform = transform.inverse();
  #pragma omp parallel
  {
#ifdef _PWN_USE_OPENMP_
    int threadNum = omp_get_thread_num();
#else // _PWN_USE_OPENMP_
    int threadNum = 0; 
    Eigen::SelfAdjointEigenSolver<Eigen::Matrix3f> eigenSolver;
#endif // _PWN_USE_OPENMP_

    for (int c=threadNum; c<indices.cols(); c+=outerStep) {
#ifdef _PWN_USE_OPENMP_
      Eigen::SelfAdjointEigenSolver<Eigen::Matrix3f> eigenSolver;
#endif // _PWN_USE_OPENMP_
      for (int r=0; r<indices.rows(); r+=_step, q++){
	int index  = indices(r,c);
	//cerr << "index("  << r <<"," << c << ")=" << index <<  endl;
	if (index<0)
	  continue;
	// determine the region
	PointWithNormal& point = points[index];
	PointWithNormalSVD& originalSVD = svds[index];
	PointWithNormalSVD svd;
	Eigen::Vector3f normal = point.normal();
	Eigen::Vector3f coord = pixelMapper.projectPoint(point.point()+Eigen::Vector3f(_worldRadius, _worldRadius, 0));
	svd._z=point(2);

	coord.head<2>()*=(1./coord(2));
	int dx = abs(c - coord[0]);
	int dy = abs(r - coord[1]);
	if (dx>_imageRadius)
	  dx = _imageRadius;
	if (dy>_imageRadius)
	  dy = _imageRadius;
	PointAccumulator acc = _integralImage.getRegion(c-dx, c+dx, r-dy, r+dy);
	svd._mean=point.point();
	if (acc.n()>_minPoints){
	  Eigen::Vector3f mean = acc.mean();
	  svd._mean = mean;
	  svd._n = acc.n();
	  Eigen::Matrix3f cov  = acc.covariance();
	  eigenSolver.compute(cov);
	  svd._U=eigenSolver.eigenvectors();
	  svd._singularValues=eigenSolver.eigenvalues();
	  if (svd._singularValues(0) <0)
	    svd._singularValues(0) = 0;
	  /*
	    cerr << "\t svd.singularValues():" << svd.singularValues() << endl;
	    cerr << "\t svd.U():" << endl << svd.U() << endl;
	    //cerr << "\t svd.curvature():" << svd.curvature() << endl;
	
	    */
	

	  normal = eigenSolver.eigenvectors().col(0).normalized();
	  if (normal.dot(inverseTransform * mean) > 0.0f)
	    normal =-normal;
	  svd.updateCurvature();
	  //cerr << "n(" << index << ") c:"  << svd.curvature() << endl << point.tail<3>() << endl;
	  if (svd.curvature()>_maxCurvature){
	    //cerr << "region: " << c-dx << " " <<  c+dx << " " <<  r-dx << " " << r+dx << " points: " << acc.n() << endl;
	    normal.setZero();
	  } 
	} else {
	  normal.setZero();
	  svd = PointWithNormalSVD();
	}
	if (svd.n() > originalSVD.n()){
	  originalSVD = svd;
	  point.setNormal(normal);
	}
      } 
    }
  }
}
コード例 #8
0
void RGBDOdometry::getIncrementalTransformation(Eigen::Vector3f & trans,
                                                Eigen::Matrix<float, 3, 3, Eigen::RowMajor> & rot,
                                                const bool & rgbOnly,
                                                const float & icpWeight,
                                                const bool & pyramid,
                                                const bool & fastOdom,
                                                const bool & so3)
{
    bool icp = !rgbOnly && icpWeight > 0;
    bool rgb = rgbOnly || icpWeight < 100;

    Eigen::Matrix<float, 3, 3, Eigen::RowMajor> Rprev = rot;
    Eigen::Vector3f tprev = trans;

    Eigen::Matrix<float, 3, 3, Eigen::RowMajor> Rcurr = Rprev;
    Eigen::Vector3f tcurr = tprev;

    if(rgb)
    {
        for(int i = 0; i < NUM_PYRS; i++)
        {
            computeDerivativeImages(nextImage[i], nextdIdx[i], nextdIdy[i]);
        }
    }

    Eigen::Matrix<double, 3, 3, Eigen::RowMajor> resultR = Eigen::Matrix<double, 3, 3, Eigen::RowMajor>::Identity();

    if(so3)
    {
        int pyramidLevel = 2;

        Eigen::Matrix<float, 3, 3, Eigen::RowMajor> R_lr = Eigen::Matrix<float, 3, 3, Eigen::RowMajor>::Identity();

        Eigen::Matrix<double, 3, 3, Eigen::RowMajor> K = Eigen::Matrix<double, 3, 3, Eigen::RowMajor>::Zero();

        K(0, 0) = intr(pyramidLevel).fx;
        K(1, 1) = intr(pyramidLevel).fy;
        K(0, 2) = intr(pyramidLevel).cx;
        K(1, 2) = intr(pyramidLevel).cy;
        K(2, 2) = 1;

        float lastError = std::numeric_limits<float>::max() / 2;
        float lastCount = std::numeric_limits<float>::max() / 2;

        Eigen::Matrix<double, 3, 3, Eigen::RowMajor> lastResultR = Eigen::Matrix<double, 3, 3, Eigen::RowMajor>::Identity();

        for(int i = 0; i < 10; i++)
        {
            Eigen::Matrix<float, 3, 3, Eigen::RowMajor> jtj;
            Eigen::Matrix<float, 3, 1> jtr;

            Eigen::Matrix<double, 3, 3, Eigen::RowMajor> homography = K * resultR * K.inverse();

            mat33 imageBasis;
            memcpy(&imageBasis.data[0], homography.cast<float>().eval().data(), sizeof(mat33));

            Eigen::Matrix<double, 3, 3, Eigen::RowMajor> K_inv = K.inverse();
            mat33 kinv;
            memcpy(&kinv.data[0], K_inv.cast<float>().eval().data(), sizeof(mat33));

            Eigen::Matrix<double, 3, 3, Eigen::RowMajor> K_R_lr = K * resultR;
            mat33 krlr;
            memcpy(&krlr.data[0], K_R_lr.cast<float>().eval().data(), sizeof(mat33));

            float residual[2];

            TICK("so3Step");
            so3Step(lastNextImage[pyramidLevel],
                    nextImage[pyramidLevel],
                    imageBasis,
                    kinv,
                    krlr,
                    sumDataSO3,
                    outDataSO3,
                    jtj.data(),
                    jtr.data(),
                    &residual[0],
                    GPUConfig::getInstance().so3StepThreads,
                    GPUConfig::getInstance().so3StepBlocks);
            TOCK("so3Step");

            lastSO3Error = sqrt(residual[0]) / residual[1];
            lastSO3Count = residual[1];

            //Converged
            if(lastSO3Error < lastError && lastCount == lastSO3Count)
            {
                break;
            }
            else if(lastSO3Error > lastError + 0.001) //Diverging
            {
                lastSO3Error = lastError;
                lastSO3Count = lastCount;
                resultR = lastResultR;
                break;
            }

            lastError = lastSO3Error;
            lastCount = lastSO3Count;
            lastResultR = resultR;

            Eigen::Vector3f delta = jtj.ldlt().solve(jtr);

            Eigen::Matrix<double, 3, 3, Eigen::RowMajor> rotUpdate = OdometryProvider::rodrigues(delta.cast<double>());

            R_lr = rotUpdate.cast<float>() * R_lr;

            for(int x = 0; x < 3; x++)
            {
                for(int y = 0; y < 3; y++)
                {
                    resultR(x, y) = R_lr(x, y);
                }
            }
        }
    }

    iterations[0] = fastOdom ? 3 : 10;
    iterations[1] = pyramid ? 5 : 0;
    iterations[2] = pyramid ? 4 : 0;

    Eigen::Matrix<float, 3, 3, Eigen::RowMajor> Rprev_inv = Rprev.inverse();
    mat33 device_Rprev_inv = Rprev_inv;
    float3 device_tprev = *reinterpret_cast<float3*>(tprev.data());

    Eigen::Matrix<double, 4, 4, Eigen::RowMajor> resultRt = Eigen::Matrix<double, 4, 4, Eigen::RowMajor>::Identity();

    if(so3)
    {
        for(int x = 0; x < 3; x++)
        {
            for(int y = 0; y < 3; y++)
            {
                resultRt(x, y) = resultR(x, y);
            }
        }
    }

    for(int i = NUM_PYRS - 1; i >= 0; i--)
    {
        if(rgb)
        {
            projectToPointCloud(lastDepth[i], pointClouds[i], intr, i);
        }

        Eigen::Matrix<double, 3, 3, Eigen::RowMajor> K = Eigen::Matrix<double, 3, 3, Eigen::RowMajor>::Zero();

        K(0, 0) = intr(i).fx;
        K(1, 1) = intr(i).fy;
        K(0, 2) = intr(i).cx;
        K(1, 2) = intr(i).cy;
        K(2, 2) = 1;

        lastRGBError = std::numeric_limits<float>::max();

        for(int j = 0; j < iterations[i]; j++)
        {
            Eigen::Matrix<double, 4, 4, Eigen::RowMajor> Rt = resultRt.inverse();

            Eigen::Matrix<double, 3, 3, Eigen::RowMajor> R = Rt.topLeftCorner(3, 3);

            Eigen::Matrix<double, 3, 3, Eigen::RowMajor> KRK_inv = K * R * K.inverse();
            mat33 krkInv;
            memcpy(&krkInv.data[0], KRK_inv.cast<float>().eval().data(), sizeof(mat33));

            Eigen::Vector3d Kt = Rt.topRightCorner(3, 1);
            Kt = K * Kt;
            float3 kt = {(float)Kt(0), (float)Kt(1), (float)Kt(2)};

            int sigma = 0;
            int rgbSize = 0;

            if(rgb)
            {
                TICK("computeRgbResidual");
                computeRgbResidual(pow(minimumGradientMagnitudes[i], 2.0) / pow(sobelScale, 2.0),
                                   nextdIdx[i],
                                   nextdIdy[i],
                                   lastDepth[i],
                                   nextDepth[i],
                                   lastImage[i],
                                   nextImage[i],
                                   corresImg[i],
                                   sumResidualRGB,
                                   maxDepthDeltaRGB,
                                   kt,
                                   krkInv,
                                   sigma,
                                   rgbSize,
                                   GPUConfig::getInstance().rgbResThreads,
                                   GPUConfig::getInstance().rgbResBlocks);
                TOCK("computeRgbResidual");
            }

            float sigmaVal = std::sqrt((float)sigma / rgbSize == 0 ? 1 : rgbSize);
            float rgbError = std::sqrt(sigma) / (rgbSize == 0 ? 1 : rgbSize);

            if(rgbOnly && rgbError > lastRGBError)
            {
                break;
            }

            lastRGBError = rgbError;
            lastRGBCount = rgbSize;

            if(rgbOnly)
            {
                sigmaVal = -1; //Signals the internal optimisation to weight evenly
            }

            Eigen::Matrix<float, 6, 6, Eigen::RowMajor> A_icp;
            Eigen::Matrix<float, 6, 1> b_icp;

            mat33 device_Rcurr = Rcurr;
            float3 device_tcurr = *reinterpret_cast<float3*>(tcurr.data());

            DeviceArray2D<float>& vmap_curr = vmaps_curr_[i];
            DeviceArray2D<float>& nmap_curr = nmaps_curr_[i];

            DeviceArray2D<float>& vmap_g_prev = vmaps_g_prev_[i];
            DeviceArray2D<float>& nmap_g_prev = nmaps_g_prev_[i];

            float residual[2];

            if(icp)
            {
                TICK("icpStep");
                icpStep(device_Rcurr,
                        device_tcurr,
                        vmap_curr,
                        nmap_curr,
                        device_Rprev_inv,
                        device_tprev,
                        intr(i),
                        vmap_g_prev,
                        nmap_g_prev,
                        distThres_,
                        angleThres_,
                        sumDataSE3,
                        outDataSE3,
                        A_icp.data(),
                        b_icp.data(),
                        &residual[0],
                        GPUConfig::getInstance().icpStepThreads,
                        GPUConfig::getInstance().icpStepBlocks);
                TOCK("icpStep");
            }

            lastICPError = sqrt(residual[0]) / residual[1];
            lastICPCount = residual[1];

            Eigen::Matrix<float, 6, 6, Eigen::RowMajor> A_rgbd;
            Eigen::Matrix<float, 6, 1> b_rgbd;

            if(rgb)
            {
                TICK("rgbStep");
                rgbStep(corresImg[i],
                        sigmaVal,
                        pointClouds[i],
                        intr(i).fx,
                        intr(i).fy,
                        nextdIdx[i],
                        nextdIdy[i],
                        sobelScale,
                        sumDataSE3,
                        outDataSE3,
                        A_rgbd.data(),
                        b_rgbd.data(),
                        GPUConfig::getInstance().rgbStepThreads,
                        GPUConfig::getInstance().rgbStepBlocks);
                TOCK("rgbStep");
            }

            Eigen::Matrix<double, 6, 1> result;
            Eigen::Matrix<double, 6, 6, Eigen::RowMajor> dA_rgbd = A_rgbd.cast<double>();
            Eigen::Matrix<double, 6, 6, Eigen::RowMajor> dA_icp = A_icp.cast<double>();
            Eigen::Matrix<double, 6, 1> db_rgbd = b_rgbd.cast<double>();
            Eigen::Matrix<double, 6, 1> db_icp = b_icp.cast<double>();

            if(icp && rgb)
            {
                double w = icpWeight;
                lastA = dA_rgbd + w * w * dA_icp;
                lastb = db_rgbd + w * db_icp;
                result = lastA.ldlt().solve(lastb);
            }
            else if(icp)
            {
                lastA = dA_icp;
                lastb = db_icp;
                result = lastA.ldlt().solve(lastb);
            }
            else if(rgb)
            {
                lastA = dA_rgbd;
                lastb = db_rgbd;
                result = lastA.ldlt().solve(lastb);
            }
            else
            {
                assert(false && "Control shouldn't reach here");
            }

            Eigen::Isometry3f rgbOdom;

            OdometryProvider::computeUpdateSE3(resultRt, result, rgbOdom);

            Eigen::Isometry3f currentT;
            currentT.setIdentity();
            currentT.rotate(Rprev);
            currentT.translation() = tprev;

            currentT = currentT * rgbOdom.inverse();

            tcurr = currentT.translation();
            Rcurr = currentT.rotation();
        }
    }

    if(rgb && (tcurr - tprev).norm() > 0.3)
    {
        Rcurr = Rprev;
        tcurr = tprev;
    }

    if(so3)
    {
        for(int i = 0; i < NUM_PYRS; i++)
        {
            std::swap(lastNextImage[i], nextImage[i]);
        }
    }

    trans = tcurr;
    rot = Rcurr;
}
コード例 #9
0
ファイル: aligner.cpp プロジェクト: 9578577/g2o_frontend
  void Aligner::align() {
    assert(_projector && "Aligner: missing _projector");
    assert(_linearizer && "Aligner: missing _linearizer");
    assert(_correspondenceFinder && "Aligner: missing _correspondenceFinder");
    assert(_referenceCloud && "Aligner: missing _referenceCloud");
    assert(_currentCloud && "Aligner: missing _currentCloud");

    struct timeval tvStart, tvEnd;
    gettimeofday(&tvStart, 0);

    // The current points are seen from the frame of the sensor
    _projector->setTransform(_currentSensorOffset);
    _projector->project(_correspondenceFinder->currentIndexImage(),
			_correspondenceFinder->currentDepthImage(),
			_currentCloud->points());
    _T = _initialGuess;
        
    for(int i = 0; i < _outerIterations; i++) {
      /************************************************************************
       *                         Correspondence Computation                   *
       ************************************************************************/

      // Compute the indices of the current scene from the point of view of the sensor
      _T.matrix().row(3) << 0.0f, 0.0f, 0.0f, 1.0f;
      _projector->setTransform(_T * _referenceSensorOffset);
      _projector->project(_correspondenceFinder->referenceIndexImage(),
			  _correspondenceFinder->referenceDepthImage(),
			  _referenceCloud->points());
    
      // Correspondences computation.  
      _correspondenceFinder->compute(*_referenceCloud, *_currentCloud, _T.inverse());
 
      /************************************************************************
       *                            Alignment                                 *
       ************************************************************************/
      Eigen::Isometry3f invT = _T.inverse();
      for(int k = 0; k < _innerIterations; k++) {      
	invT.matrix().block<1, 4>(3, 0) << 0.0f, 0.0f, 0.0f, 1.0f;
	Matrix6f H;
	Vector6f b;

	_linearizer->setT(invT);
	_linearizer->update();
	H = _linearizer->H() + Matrix6f::Identity();
	b = _linearizer->b();
	H += Matrix6f::Identity() * 1000.0f;

	// Add the priors
	for(size_t j = 0; j < _priors.size(); j++) {
	  const SE3Prior *prior = _priors[j];
	  Vector6f priorError = prior->error(invT);
	  Matrix6f priorJacobian = prior->jacobian(invT);
	  Matrix6f priorInformationRemapped = prior->errorInformation(invT);

	  Matrix6f Hp = priorJacobian.transpose() * priorInformationRemapped * priorJacobian;
	  Vector6f bp = priorJacobian.transpose() * priorInformationRemapped * priorError;

	  H += Hp;
	  b += bp;
	}
      
	Vector6f dx = H.ldlt().solve(-b);
	Eigen::Isometry3f dT = v2t(dx);
	invT = dT * invT;
      }
      
      _T = invT.inverse();
      _T = v2t(t2v(_T));
      _T.matrix().block<1, 4>(3, 0) << 0.0f, 0.0f, 0.0f, 1.0f;
    }

    gettimeofday(&tvEnd, 0);
    double tStart = tvStart.tv_sec * 1000.0f + tvStart.tv_usec * 0.001f;
    double tEnd = tvEnd.tv_sec * 1000.0f + tvEnd.tv_usec * 0.001f;
    _totalTime = tEnd - tStart;
    _error = _linearizer->error();
    _inliers = _linearizer->inliers();

    _computeStatistics(_mean, _omega, _translationalEigenRatio, _rotationalEigenRatio);
    if (_rotationalEigenRatio > _rotationalMinEigenRatio || 
	_translationalEigenRatio > _translationalMinEigenRatio) {
      if (_debug) {
	cerr << endl;
	cerr << "************** WARNING SOLUTION MIGHT BE INVALID (eigenratio failure) **************" << endl;
	cerr << "tr: " << _translationalEigenRatio << " rr: " << _rotationalEigenRatio << endl;
	cerr << "************************************************************************************" << endl;
      }
    } 
    else {
      if (_debug) {
	cerr << "************** I FOUND SOLUTION VALID SOLUTION   (eigenratio ok) *******************" << endl;
	cerr << "tr: " << _translationalEigenRatio << " rr: " << _rotationalEigenRatio << endl;
	cerr << "************************************************************************************" << endl;
      }
    }
    if (_debug) {
      cout << "Solution statistics in (t, mq): " << endl;
      cout << "mean: " << _mean.transpose() << endl;
      cout << "Omega: " << endl;
      cout << _omega << endl;
    }
  }