getting better
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ec047ccd08
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e8db2b6b9b
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@ -192,18 +192,12 @@ class SmartStereoProjectionFactorPP : public SmartStereoProjectionFactor {
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const Values& values, const double lambda = 0.0, bool diagonalDamping =
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false) const {
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KeyVector allKeys; // includes body poses and *unique* extrinsic poses
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allKeys.insert(allKeys.end(), keys_.begin(), keys_.end());
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size_t numKeys = allKeys.size();
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size_t nrKeys = keys_.size();
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// Create structures for Hessian Factors
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KeyVector js;
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std::vector<Matrix> Gs(numKeys * (numKeys + 1) / 2);
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std::vector<Vector> gs(numKeys);
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// for(size_t i=0; i<numKeys;i++){
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// std::cout <<"key: " << DefaultKeyFormatter(allKeys[i]) << std::endl;
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// }
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std::vector<Matrix> Gs(nrKeys * (nrKeys + 1) / 2);
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std::vector<Vector> gs(nrKeys);
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if (this->measured_.size() != cameras(values).size())
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throw std::runtime_error("SmartStereoProjectionHessianFactor: this->"
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@ -218,25 +212,16 @@ class SmartStereoProjectionFactorPP : public SmartStereoProjectionFactor {
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m = Matrix::Zero(DimPose,DimPose);
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for(Vector& v: gs)
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v = Vector::Zero(DimPose);
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return boost::make_shared<RegularHessianFactor<DimPose> >(allKeys,
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return boost::make_shared<RegularHessianFactor<DimPose> >(keys_,
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Gs, gs, 0.0);
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}
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// std::cout << "result_" << *result_ << std::endl;
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// std::cout << "result_2" << result_ << std::endl;
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// Jacobian could be 3D Point3 OR 2D Unit3, difference is E.cols().
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FBlocks Fs;
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Matrix F, E;
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Vector b;
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computeJacobiansWithTriangulatedPoint(Fs, E, b, values);
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// std::cout << "Dim "<< Dim << std::endl;
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// std::cout << "numKeys "<< numKeys << std::endl;
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//
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// std::cout << "Fs.size() = " << Fs.size() << std::endl;
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// std::cout << "E = " << E << std::endl;
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// std::cout << "b = " << b << std::endl;
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// Whiten using noise model
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// std::cout << "noise model1 \n " << std::endl;
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noiseModel_->WhitenSystem(E, b);
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@ -257,45 +242,75 @@ class SmartStereoProjectionFactorPP : public SmartStereoProjectionFactor {
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SymmetricBlockMatrix augmentedHessian = //
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Cameras::SchurComplement<3,Dim>(Fs, E, P, b);
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// KeyVector sorted_body_P_cam_keys(body_P_cam_keys_); // make a copy that we can edit
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// std::sort(sorted_body_P_cam_keys.begin(), sorted_body_P_cam_keys.end()); // required by unique
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// std::unique(sorted_body_P_cam_keys.begin(), sorted_body_P_cam_keys.end());
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// allKeys.insert(allKeys.end(), sorted_body_P_cam_keys.begin(), sorted_body_P_cam_keys.end());
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std::vector<DenseIndex> dims(numKeys + 1); // this also includes the b term
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std::vector<DenseIndex> dims(nrKeys + 1); // this also includes the b term
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std::fill(dims.begin(), dims.end() - 1, 6);
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dims.back() = 1;
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size_t nrKeysNonUnique = w_P_body_keys_.size() + body_P_cam_keys_.size();
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if ( numKeys == nrKeysNonUnique ){ // 1 calibration per camera
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SymmetricBlockMatrix augmentedHessianPP = SymmetricBlockMatrix(dims, Matrix(augmentedHessian.selfadjointView()));
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return boost::make_shared<RegularHessianFactor<DimPose> >(allKeys,
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augmentedHessianPP);
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}else{
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Matrix augmentedHessianMatrixPP = Matrix(augmentedHessian.selfadjointView());
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Matrix associationMatrix = Matrix::Zero( numKeys, nrKeysNonUnique ); // association from unique keys to vector with potentially repeated keys
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std::cout << "Linearize" << std::endl;
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for(size_t i=0; i<numKeys;i++){
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for(size_t j=0; j<nrKeysNonUnique;k++){
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if ( keys_[i] == )
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// std::cout <<"key: " << DefaultKeyFormatter(allKeys[i]) << std::endl;
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size_t nrNonuniqueKeys = w_P_body_keys_.size() + body_P_cam_keys_.size();
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SymmetricBlockMatrix augmentedHessianPP;
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if ( nrKeys == nrNonuniqueKeys ){ // 1 calibration per camera
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augmentedHessianPP = SymmetricBlockMatrix(dims, Matrix(augmentedHessian.selfadjointView()));
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}else{
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std::vector<DenseIndex> nonuniqueDims(nrNonuniqueKeys + 1); // this also includes the b term
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std::fill(nonuniqueDims.begin(), nonuniqueDims.end() - 1, 6);
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nonuniqueDims.back() = 1;
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augmentedHessian = SymmetricBlockMatrix(nonuniqueDims, Matrix(augmentedHessian.selfadjointView()));
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// these are the keys that correspond to the blocks in augmentedHessian (output of SchurComplement)
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KeyVector nonuniqueKeys;
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for(size_t i=0; i < w_P_body_keys_.size();i++){
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nonuniqueKeys.push_back(w_P_body_keys_.at(i));
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nonuniqueKeys.push_back(body_P_cam_keys_.at(i));
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}
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// get map from key to location in the new augmented Hessian matrix
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std::map<Key,size_t> keyToSlotMap;
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for(size_t k=0; k<nrKeys;k++){
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keyToSlotMap[keys_[k]] = k;
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}
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std::cout << "linearize" << std::endl;
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for(size_t i=0; i<nrKeys;i++){
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std::cout <<"key: " << DefaultKeyFormatter(keys_[i]);
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std::cout <<" key slot: " << keyToSlotMap[keys_[i]] << std::endl;
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}
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for(size_t i=0; i<nrNonuniqueKeys;i++){
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std::cout <<"key: " << DefaultKeyFormatter(nonuniqueKeys[i]);
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std::cout <<" key slot: " << keyToSlotMap[nonuniqueKeys[i]] << std::endl;
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}
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// initialize matrix to zero
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augmentedHessianPP = SymmetricBlockMatrix(dims, Matrix::Zero(6*nrKeys+1,6*nrKeys+1));
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std::cout <<" start for loop: " << std::endl;
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// add contributions for each key: note this loops over the hessian with nonUnique keys (augmentedHessian)
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for(size_t i=0; i<nrNonuniqueKeys;i++){ // rows
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Key key_i = nonuniqueKeys.at(i);
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std::cout <<" start for loop i: " << std::endl;
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for(size_t j=0; j<nrNonuniqueKeys;j++){ // cols
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std::cout <<" start for loop j: " << std::endl;
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Key key_j = nonuniqueKeys.at(j);
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std::cout <<"key_i: " << DefaultKeyFormatter(key_i);
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std::cout <<" key_j: " << DefaultKeyFormatter(key_j);
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std::cout <<" start for loop --: " << std::endl;
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if(i==j){
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std::cout <<" i=0: " << std::endl;
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augmentedHessianPP.updateDiagonalBlock( keyToSlotMap[key_i] , augmentedHessian.diagonalBlock(i));
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}else if(i < j){
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std::cout <<" i<j: " << std::endl;
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augmentedHessianPP.updateOffDiagonalBlock( keyToSlotMap[key_i] , keyToSlotMap[key_j],
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augmentedHessian.aboveDiagonalBlock(i,j));
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}
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else{
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std::cout <<" i>j: " << std::endl;
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augmentedHessianPP.updateOffDiagonalBlock( keyToSlotMap[key_i] , keyToSlotMap[key_j],
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augmentedHessian.aboveDiagonalBlock(j,i));
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}
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}
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}
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for (size_t i=0; i < w_P_body_keys_.size() + body_P_cam_keys_.size(); i++){
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// create map of unique keys
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}
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std::vector<Matrix> Gs(numKeys * (numKeys + 1) / 2);
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std::vector<Vector> gs(numKeys);
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for(Matrix& m: Gs)
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m = Matrix::Zero(DimPose,DimPose);
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for(Vector& v: gs)
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v = Vector::Zero(DimPose);
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double e = augmentedHessianMatrixPP( augmentedHessianMatrixPP.rows()-1, augmentedHessianMatrixPP.cols()-1 );
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return boost::make_shared<RegularHessianFactor<DimPose> >(allKeys,
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Gs, gs, e);
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}
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return boost::make_shared<RegularHessianFactor<DimPose> >(keys_, augmentedHessianPP);
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//std::cout << "Matrix(augmentedHessian.selfadjointView()) \n" << Matrix(augmentedHessian.selfadjointView()) <<std::endl;
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}
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