Bundle.cc

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// Copyright 2008 Isis Innovation Limited
#include "Bundle.h"
#include "MEstimator.h"
#include <TooN/helpers.h>
#include <TooN/Cholesky.h>
#include <fstream>
#include <iomanip>
#include <gvars3/instances.h>

using namespace GVars3;
using namespace std;

#ifdef WIN32
inline bool isnan(double d) {return !(d==d);}
#endif

#include "settingsCustom.h"

#define cout if(*mgvnBundleCout) cout

// Some inlines which replace standard matrix multiplications 
// with LL-triangle-only versions.
inline void BundleTriangle_UpdateM6U_LL(Matrix<6> &m6U, Matrix<2,6> &m26A)
{
  for(int r=0; r<6; r++)
    for(int c=0; c<=r; c++)
      m6U(r,c)+= m26A.T()(r,0)*m26A(0,c) + m26A.T()(r,1)*m26A(1,c);
}
inline void BundleTriangle_UpdateM3V_LL(Matrix<3> &m3V, Matrix<2,3> &m23B)
{
  for(int r=0; r<3; r++) 
    for(int c=0; c<=r; c++) 
      m3V(r,c)+= m23B.T()(r,0)*m23B(0,c) + m23B.T()(r,1)*m23B(1,c); 
}

// Constructor copies MapMaker's camera parameters
Bundle::Bundle(const ATANCamera &TCam)
  : mCamera(TCam)
{
  mnCamsToUpdate = 0;
  mnStartRow = 0;
  GV3::Register(mgvnMaxIterations, "Bundle.MaxIterations", BUNDLE_MAX_ITERATIONS,  SILENT);
  GV3::Register(mgvdUpdateConvergenceLimit, "Bundle.UpdateSquaredConvergenceLimit", BUNDLE_UPDATE_SQUARED_CONV_LIMIT, SILENT);
  GV3::Register(mgvnBundleCout, "Bundle.Cout", BUNDLE_COUT, SILENT);
};

// Add a camera to the system, return value is the bundle adjuster's ID for the camera
int Bundle::AddCamera(SE3<> se3CamFromWorld, bool bFixed)
{
  int n = mvCameras.size();
  Camera c;
  c.bFixed = bFixed;
  c.se3CfW = se3CamFromWorld;
  if(!bFixed)
    {
      c.nStartRow = mnStartRow;
      mnStartRow += 6;
      mnCamsToUpdate++;
    }
  else
    c.nStartRow = -999999999; 
  mvCameras.push_back(c);
   
  return n;
}

// Add a map point to the system, return value is the bundle adjuster's ID for the point
int Bundle::AddPoint(Vector<3> v3Pos)
{
  int n = mvPoints.size();
  Point p;
  if(isnan(v3Pos * v3Pos))
    {
      cerr << " You sucker, tried to give me a nan " << v3Pos << endl;
      v3Pos = Zeros;
    }
  p.v3Pos = v3Pos;
  mvPoints.push_back(p);
  return n;
}

// Add a measurement of one point with one camera
void Bundle::AddMeas(int nCam, int nPoint, Vector<2> v2Pos, double dSigmaSquared)
{
  assert(nCam < (int) mvCameras.size());
  assert(nPoint < (int) mvPoints.size());
  mvPoints[nPoint].nMeasurements++;
  mvPoints[nPoint].sCameras.insert(nCam);
  Meas m;
  m.p = nPoint;
  m.c = nCam;
  m.v2Found = v2Pos;
  m.dSqrtInvNoise = sqrt(1.0 / dSigmaSquared);
  mMeasList.push_back(m);
}

// Zero temporary quantities stored in cameras and points
void Bundle::ClearAccumulators()
{
  for(size_t i=0; i<mvPoints.size(); ++i)
    {
      mvPoints[i].m3V = Zeros;
      mvPoints[i].v3EpsilonB = Zeros;
    }
  for(size_t i=0; i<mvCameras.size(); ++i)
    {
      mvCameras[i].m6U = Zeros;
      mvCameras[i].v6EpsilonA = Zeros;
    }
}

// Perform bundle adjustment. The parameter points to a signal bool 
// which mapmaker will set to high if another keyframe is incoming
// and bundle adjustment needs to be aborted.
// Returns number of accepted iterations if all good, negative 
// value for big error.
int Bundle::Compute(bool *pbAbortSignal)
{
  mpbAbortSignal = pbAbortSignal;

  // Some speedup data structures
  GenerateMeasLUTs();
  GenerateOffDiagScripts();

  // Initially behave like gauss-newton
  mdLambda = 0.0001;
  mdLambdaFactor = 2.0;
  mbConverged = false;
  mbHitMaxIterations = false;
  mnCounter = 0;
  mnAccepted = 0;
  
  // What MEstimator are we using today?
  static gvar3<string> gvsMEstimator("BundleMEstimator", BUNDLE_M_ESTIMATOR, SILENT);
  
  while(!mbConverged  && !mbHitMaxIterations && !*pbAbortSignal)
    {
      bool bNoError;
      if(*gvsMEstimator == "Cauchy")
        bNoError = Do_LM_Step<Cauchy>(pbAbortSignal);  
      else if(*gvsMEstimator == "Tukey")
        bNoError = Do_LM_Step<Tukey>(pbAbortSignal);
      else if(*gvsMEstimator == "Huber")
        bNoError = Do_LM_Step<Huber>(pbAbortSignal);
      else
        {
          cout << "Invalid BundleMEstimator selected !! " << endl;
          cout << "Defaulting to Tukey." << endl;
          *gvsMEstimator = "Tukey";
          bNoError = Do_LM_Step<Tukey>(pbAbortSignal);
        };
      
      if(!bNoError)
        return -1;
    }
  
  if(mbHitMaxIterations)
    cout << "  Hit max iterations." << endl;
  cout << "Final Sigma Squared: " << mdSigmaSquared << " (= " << sqrt(mdSigmaSquared) / 4.685 << " pixels.)" << endl;
  return mnAccepted;
};

// Reproject a single measurement, find error
inline void Bundle::ProjectAndFindSquaredError(Meas &meas)
{
  Camera &cam = mvCameras[meas.c];
  Point &point = mvPoints[meas.p];
  
  // Project the point.
  meas.v3Cam = cam.se3CfW * point.v3Pos;
  if(meas.v3Cam[2] <= 0)
    {
      meas.bBad = true;
      return;
    }
  meas.bBad = false;
  Vector<2> v2ImPlane = project(meas.v3Cam);
  Vector<2> v2Image   = mCamera.Project(v2ImPlane);
  meas.m2CamDerivs = mCamera.GetProjectionDerivs();
  meas.v2Epsilon = meas.dSqrtInvNoise * (meas.v2Found - v2Image);
  meas.dErrorSquared = meas.v2Epsilon * meas.v2Epsilon;
}

template<class MEstimator>
bool Bundle::Do_LM_Step(bool *pbAbortSignal)
{
  // Reset all accumulators to zero
  ClearAccumulators();

  //  Do a LM step according to Hartley and Zisserman Algo A6.4 in MVG 2nd Edition
  //  Actual work starts a bit further down - first we have to work out the 
  //  projections and errors for each point, so we can do tukey reweighting
  vector<double> vdErrorSquared;
  for(list<Meas>::iterator itr = mMeasList.begin(); itr!=mMeasList.end(); itr++)
    {
      Meas &meas = *itr;
      ProjectAndFindSquaredError(meas);
      if(!meas.bBad)
        vdErrorSquared.push_back(meas.dErrorSquared);
    };
  
  // Projected all points and got vector of errors; find the median, 
  // And work out robust estimate of sigma, then scale this for the tukey
  // estimator
  // BUG? 
  if(vdErrorSquared.size() < 1) return false;
  mdSigmaSquared = MEstimator::FindSigmaSquared(vdErrorSquared);

  // Initially the median error might be very small - set a minimum
  // value so that good measurements don't get erased!
  static gvar3<double> gvdMinSigma("Bundle.MinTukeySigma", BUNDLE_MIN_TUKEY_SIGMA, SILENT);
  const double dMinSigmaSquared = *gvdMinSigma * *gvdMinSigma;
  if(mdSigmaSquared < dMinSigmaSquared)
    mdSigmaSquared = dMinSigmaSquared;


  //  OK - good to go! weights can now be calced on second run through the loop.
  //  Back to Hartley and Zisserman....
  //  `` (i) Compute the derivative matrices Aij = [dxij/daj]
  //      and Bij = [dxij/dbi] and the error vectors eij''
  //
  //  Here we do this by looping over all measurements
  // 
  //  While we're here, might as well update the accumulators U, ea, B, eb
  //  from part (ii) as well, and let's calculate Wij while we're here as well.
  
  double dCurrentError = 0.0;
  for(list<Meas>::iterator itr = mMeasList.begin(); itr!=mMeasList.end(); itr++)
    {
      Meas &meas = *itr;
      Camera &cam = mvCameras[meas.c];
      Point &point = mvPoints[meas.p];
      
      // Project the point.
      // We've done this before - results are still cached in meas.
      if(meas.bBad)
        {
          dCurrentError += 1.0;
          continue;
        };
      
      // What to do with the weights? The easiest option is to independently weight
      // The two jacobians, A and B, with sqrt of the tukey weight w;
      // And also weight the error vector v2Epsilon.
      // That makes everything else automatic.
      // Calc the square root of the tukey weight:
      double dWeight= MEstimator::SquareRootWeight(meas.dErrorSquared, mdSigmaSquared);
      // Re-weight error:
      meas.v2Epsilon = dWeight * meas.v2Epsilon;
      
      if(dWeight == 0)
        {
          meas.bBad = true;  
          dCurrentError += 1.0;
          continue;
        }
      
      dCurrentError += MEstimator::ObjectiveScore(meas.dErrorSquared, mdSigmaSquared);
      
      // To re-weight the jacobians, I'll just re-weight the camera param matrix
      // This is only used for the jacs and will save a few fmuls
      Matrix<2> m2CamDerivs = dWeight * meas.m2CamDerivs;
      
      const double dOneOverCameraZ = 1.0 / meas.v3Cam[2];
      const Vector<4> v4Cam = unproject(meas.v3Cam);
      
      // Calculate A: (the proj derivs WRT the camera)
      if(cam.bFixed)
        meas.m26A = Zeros;
      else 
        {
          for(int m=0;m<6;m++)
            {
              const Vector<4> v4Motion = SE3<>::generator_field(m, v4Cam);
              Vector<2> v2CamFrameMotion;
              v2CamFrameMotion[0] = (v4Motion[0] - v4Cam[0] * v4Motion[2] * dOneOverCameraZ) * dOneOverCameraZ;
              v2CamFrameMotion[1] = (v4Motion[1] - v4Cam[1] * v4Motion[2] * dOneOverCameraZ) * dOneOverCameraZ;
              meas.m26A.T()[m] = meas.dSqrtInvNoise * m2CamDerivs * v2CamFrameMotion;
            };
        }
      
      // Calculate B: (the proj derivs WRT the point)
      for(int m=0;m<3;m++)
        {
          const Vector<3> v3Motion = cam.se3CfW.get_rotation().get_matrix().T()[m];
          Vector<2> v2CamFrameMotion;
          v2CamFrameMotion[0] = (v3Motion[0] - v4Cam[0] * v3Motion[2] * dOneOverCameraZ) * dOneOverCameraZ;
          v2CamFrameMotion[1] = (v3Motion[1] - v4Cam[1] * v3Motion[2] * dOneOverCameraZ) * dOneOverCameraZ;
          meas.m23B.T()[m] = meas.dSqrtInvNoise * m2CamDerivs * v2CamFrameMotion;
        };
      
      // Update the accumulators
      if(!cam.bFixed)
        {
          //      cam.m6U += meas.m26A.T() * meas.m26A;           // SLOW SLOW this matrix is symmetric
          BundleTriangle_UpdateM6U_LL(cam.m6U, meas.m26A);
          cam.v6EpsilonA += meas.m26A.T() * meas.v2Epsilon;
          // NOISE COVAR OMITTED because it's the 2-Identity
        }
      
      //            point.m3V += meas.m23B.T() * meas.m23B;             // SLOW-ish this is symmetric too
      BundleTriangle_UpdateM3V_LL(point.m3V, meas.m23B);
      point.v3EpsilonB += meas.m23B.T() * meas.v2Epsilon;
      
      if(cam.bFixed)
        meas.m63W = Zeros;
      else
        meas.m63W = meas.m26A.T() * meas.m23B;
    }
  
  // OK, done (i) and most of (ii) except calcing Yij; this depends on Vi, which should 
  // be finished now. So we can find V*i (by adding lambda) and then invert.
  // The next bits depend on mdLambda! So loop this next bit until error goes down.
  double dNewError = dCurrentError + 9999;
  while(dNewError > dCurrentError && !mbConverged && !mbHitMaxIterations && !*pbAbortSignal)
    {
      // Rest of part (ii) : find V*i inverse
      for(vector<Point>::iterator itr = mvPoints.begin(); itr!=mvPoints.end(); itr++)
        {
          Point &point = *itr;
          Matrix<3> m3VStar = point.m3V;
          if(m3VStar[0][0] * m3VStar[1][1] * m3VStar[2][2] == 0)
            point.m3VStarInv = Zeros;
          else
            {
              // Fill in the upper-r triangle from the LL;
              m3VStar[0][1] = m3VStar[1][0];
              m3VStar[0][2] = m3VStar[2][0];
              m3VStar[1][2] = m3VStar[2][1];
               
              for(int i=0; i<3; i++)
                m3VStar[i][i] *= (1.0 + mdLambda);
              Cholesky<3> chol(m3VStar);
              point.m3VStarInv = chol.get_inverse();
            };
        }

      // Done part (ii), except calculating Yij;
      // But we can do this inline when we calculate S in part (iii).
      
      // Part (iii): Construct the the big block-matrix S which will be inverted.
      Matrix<> mS(mnCamsToUpdate * 6, mnCamsToUpdate * 6);
      mS = Zeros;
      Vector<> vE(mnCamsToUpdate * 6);
      vE = Zeros;

      Matrix<6> m6; // Temp working space
      Vector<6> v6; // Temp working space
      
      // Calculate on-diagonal blocks of S (i.e. only one camera at a time:)
      for(unsigned int j=0; j<mvCameras.size(); j++)
        {
          Camera &cam_j = mvCameras[j];
          if(cam_j.bFixed) continue;
          int nCamJStartRow = cam_j.nStartRow;
          
          // First, do the diagonal elements.
          //m6= cam_j.m6U;     // can't do this anymore because cam_j.m6U is LL!!
          for(int r=0; r<6; r++)
            {
              for(int c=0; c<r; c++)
                m6[r][c] = m6[c][r] = cam_j.m6U[r][c];
              m6[r][r] = cam_j.m6U[r][r];
            };
          
          for(int nn = 0; nn< 6; nn++)
            m6[nn][nn] *= (1.0 + mdLambda);
          
          v6 = cam_j.v6EpsilonA;
          
          vector<Meas*> &vMeasLUTj = mvMeasLUTs[j];
          // Sum over measurements (points):
          for(unsigned int i=0; i<mvPoints.size(); i++)
            {
              Meas* pMeas = vMeasLUTj[i];
              if(pMeas == NULL || pMeas->bBad)
                continue;
              m6 -= pMeas->m63W * mvPoints[i].m3VStarInv * pMeas->m63W.T();  // SLOW SLOW should by 6x6sy
              v6 -= pMeas->m63W * (mvPoints[i].m3VStarInv * mvPoints[i].v3EpsilonB);
            }
          mS.slice(nCamJStartRow, nCamJStartRow, 6, 6) = m6;
          vE.slice(nCamJStartRow,6) = v6;
        }
      
      // Now find off-diag elements of S. These are camera-point-camera combinations, of which there are lots.
      // New code which doesn't waste as much time finding i-jk pairs; these are pre-stored in a per-i list.
      for(unsigned int i=0; i<mvPoints.size(); i++)
        {
          Point &p = mvPoints[i];
          int nCurrentJ = -1;
          int nJRow = -1;
          Meas* pMeas_ij;
          Meas* pMeas_ik;
          Matrix<6,3> m63_MIJW_times_m3VStarInv;
          
          for(vector<OffDiagScriptEntry>::iterator it=p.vOffDiagonalScript.begin();
              it!=p.vOffDiagonalScript.end();
              it++)
            {
              OffDiagScriptEntry &e = *it;
              pMeas_ik = mvMeasLUTs[e.k][i];
              if(pMeas_ik == NULL || pMeas_ik->bBad)
                continue;
              if(e.j != nCurrentJ)
                {
                  pMeas_ij = mvMeasLUTs[e.j][i];
                  if(pMeas_ij == NULL || pMeas_ij->bBad)
                    continue;
                  nCurrentJ = e.j;
                  nJRow = mvCameras[e.j].nStartRow;
                  m63_MIJW_times_m3VStarInv = pMeas_ij->m63W * p.m3VStarInv;
                }
              int nKRow = mvCameras[pMeas_ik->c].nStartRow;
#ifndef WIN32
                  mS.slice(nJRow, nKRow, 6, 6) -= m63_MIJW_times_m3VStarInv * pMeas_ik->m63W.T();
#else
                  Matrix<6> m = mS.slice(nJRow, nKRow, 6, 6);
                  m -= m63_MIJW_times_m3VStarInv * pMeas_ik->m63W.T();
          mS.slice(nJRow, nKRow, 6, 6) = m;
#endif
              assert(nKRow < nJRow);
            }
        }
      
      // Did this purely LL triangle - now update the TR bit as well!
      // (This is actually unneccessary since the lapack cholesky solver
      // uses only one triangle, but I'm leaving it in here anyway.)
      for(int i=0; i<mS.num_rows(); i++)
        for(int j=0; j<i; j++)
          mS[j][i] = mS[i][j];
      
      // Got fat matrix S and vector E from part(iii). Now Cholesky-decompose
      // the matrix, and find the camera update vector.
      Vector<> vCamerasUpdate(mS.num_rows());
      vCamerasUpdate = Cholesky<>(mS).backsub(vE);
      
      // Part (iv): Compute the map updates
      Vector<> vMapUpdates(mvPoints.size() * 3);
      for(unsigned int i=0; i<mvPoints.size(); i++)
        {
          Vector<3> v3Sum;
          v3Sum = Zeros;
          for(unsigned int j=0; j<mvCameras.size(); j++)
            {
              Camera &cam = mvCameras[j];
              if(cam.bFixed)
                continue;
              Meas *pMeas = mvMeasLUTs[j][i];
              if(pMeas == NULL || pMeas->bBad)
                continue;
              v3Sum+=pMeas->m63W.T() * vCamerasUpdate.slice(cam.nStartRow,6);
            }
          Vector<3> v3 = mvPoints[i].v3EpsilonB - v3Sum;
          vMapUpdates.slice(i * 3, 3) = mvPoints[i].m3VStarInv * v3;
          if(isnan(vMapUpdates.slice(i * 3, 3) * vMapUpdates.slice(i * 3, 3)))
            {
              cerr << "NANNERY! " << endl;
              cerr << mvPoints[i].m3VStarInv << endl;
            };
        }
      
      // OK, got the two update vectors.
      // First check for convergence..
      // (this is a very poor convergence test)
      double dSumSquaredUpdate = vCamerasUpdate * vCamerasUpdate + vMapUpdates * vMapUpdates;
      if(dSumSquaredUpdate< *mgvdUpdateConvergenceLimit)
        mbConverged = true;
      
      // Now re-project everything and measure the error;
      // NB we don't keep these projections, SLOW, bit of a waste.
      
      // Temp versions of updated pose and pos:
      for(unsigned int j=0; j<mvCameras.size(); j++)
        {
          if(mvCameras[j].bFixed)
            mvCameras[j].se3CfWNew = mvCameras[j].se3CfW;
          else
            mvCameras[j].se3CfWNew = SE3<>::exp(vCamerasUpdate.slice(mvCameras[j].nStartRow, 6)) * mvCameras[j].se3CfW;
        }
      for(unsigned int i=0; i<mvPoints.size(); i++)
        mvPoints[i].v3PosNew = mvPoints[i].v3Pos + vMapUpdates.slice(i*3, 3);
      // Calculate new error by re-projecting, doing tukey, etc etc:
      dNewError = FindNewError<MEstimator>();
      
      cout <<setprecision(1) << "L" << mdLambda << setprecision(3) <<  "\tOld " << dCurrentError << "  New " << dNewError << "  Diff " << dCurrentError - dNewError << "\t";
      
      // Was the step good? If not, modify lambda and try again!!
      // (if it was good, will break from this loop.)
      if(dNewError > dCurrentError)
        {
          cout << " TRY AGAIN " << endl;
          ModifyLambda_BadStep();
        };
      
      mnCounter++;
      if(mnCounter >= *mgvnMaxIterations)
        mbHitMaxIterations = true;
    }   // End of while error too big loop
  
  if(dNewError < dCurrentError) // Was the last step a good one?
    {
      cout << " WINNER            ------------ " << endl;
      // Woo! got somewhere. Update lambda and make changes permanent.
      ModifyLambda_GoodStep();
      for(unsigned int j=0; j<mvCameras.size(); j++)
        mvCameras[j].se3CfW = mvCameras[j].se3CfWNew;
      for(unsigned int i=0; i<mvPoints.size(); i++)
        mvPoints[i].v3Pos = mvPoints[i].v3PosNew; 
      mnAccepted++;
    }
  
  // Finally, ditch all the outliers.
  vector<list<Meas>::iterator> vit;
  for(list<Meas>::iterator itr = mMeasList.begin(); itr!=mMeasList.end(); itr++)
    if(itr->bBad)
      {
        vit.push_back(itr);
        mvOutlierMeasurementIdx.push_back(make_pair(itr->p, itr->c));
        mvPoints[itr->p].nOutliers++;
        mvMeasLUTs[itr->c][itr->p] = NULL;
      };
  
  for(unsigned int i=0; i<vit.size(); i++)
    mMeasList.erase(vit[i]);
  
  cout << "Nuked " << vit.size() << " measurements." << endl;
  return true;
}

// Find the new total error if cameras and points used their 
// new coordinates
template<class MEstimator>
double Bundle::FindNewError()
{
  ofstream ofs;
  double dNewError = 0;
  vector<double> vdErrorSquared;
  for(list<Meas>::iterator itr = mMeasList.begin(); itr!=mMeasList.end(); itr++)
    {
      Meas &meas = *itr;
      // Project the point.
      Vector<3> v3Cam = mvCameras[meas.c].se3CfWNew * mvPoints[meas.p].v3PosNew;
      if(v3Cam[2] <= 0)
        {
          dNewError += 1.0;
          cout << ".";
          continue;
        };
      Vector<2> v2ImPlane = project(v3Cam);
      Vector<2> v2Image   = mCamera.Project(v2ImPlane);
      Vector<2> v2Error =   meas.dSqrtInvNoise * (meas.v2Found - v2Image);
      double dErrorSquared = v2Error * v2Error;
      dNewError += MEstimator::ObjectiveScore(dErrorSquared, mdSigmaSquared);
    }
  return dNewError;
}

// Optimisation: make a bunch of tables, one per camera
// which point to measurements (if any) for each point
// This is faster than a std::map lookup.
void Bundle::GenerateMeasLUTs()
{
  mvMeasLUTs.clear();
  for(unsigned int nCam = 0; nCam < mvCameras.size(); nCam++)
    {
      mvMeasLUTs.push_back(vector<Meas*>());
      mvMeasLUTs.back().resize(mvPoints.size(), NULL);
    };
  for(list<Meas>::iterator it = mMeasList.begin(); it!=mMeasList.end(); it++)
    mvMeasLUTs[it->c][it->p] =  &(*it);
}

// Optimisation: make a per-point list of all
// observation camera-camera pairs; this is then
// scanned to make the off-diagonal elements of matrix S
void Bundle::GenerateOffDiagScripts()
{
  for(unsigned int i=0; i<mvPoints.size(); i++)
    {
      Point &p = mvPoints[i];
      p.vOffDiagonalScript.clear();
      for(set<int>::iterator it_j = p.sCameras.begin(); it_j!=p.sCameras.end(); it_j++)
        {
          int j = *it_j;
          if(mvCameras[j].bFixed)
            continue;
          Meas *pMeas_j = mvMeasLUTs[j][i];
          assert(pMeas_j != NULL);
          
          for(set<int>::iterator it_k = p.sCameras.begin(); it_k!=it_j; it_k++)
            {
              int k = *it_k;
              if(mvCameras[k].bFixed)
                continue;
              
              Meas *pMeas_k = mvMeasLUTs[k][i];
              assert(pMeas_k != NULL);
              
              OffDiagScriptEntry e;
              e.j = j;
              e.k = k;
              p.vOffDiagonalScript.push_back(e);
            }
        }
    }
}

void Bundle::ModifyLambda_GoodStep()
{
  mdLambdaFactor = 2.0;
  mdLambda *= 0.3;
};

void Bundle::ModifyLambda_BadStep()
{
  mdLambda = mdLambda * mdLambdaFactor;
  mdLambdaFactor = mdLambdaFactor * 2;
};


Vector<3> Bundle::GetPoint(int n)
{
  return mvPoints.at(n).v3Pos;
}

SE3<> Bundle::GetCamera(int n)
{
  return mvCameras.at(n).se3CfW;
}

set<int> Bundle::GetOutliers()
{
  set<int> sOutliers;
  set<int>::iterator hint = sOutliers.begin();
  for(unsigned int i=0; i<mvPoints.size(); i++)
    {
      Point &p = mvPoints[i];
      if(p.nMeasurements > 0 && p.nMeasurements == p.nOutliers)
        hint = sOutliers.insert(hint, i);
    }
  return sOutliers;
};


vector<pair<int, int> > Bundle::GetOutlierMeasurements()
{
  return mvOutlierMeasurementIdx;
}