avoided warning
parent
b0cca0e4f0
commit
b42a234c66
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@ -126,7 +126,7 @@ GaussianFactorGraph LevenbergMarquardtOptimizer::buildDampedSystem(
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if (params_.diagonalDamping && params_.reuse_diagonal_ == false) {
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if (params_.diagonalDamping && params_.reuse_diagonal_ == false) {
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state_.hessianDiagonal = linear.hessianDiagonal();
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state_.hessianDiagonal = linear.hessianDiagonal();
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BOOST_FOREACH(Vector& v, state_.hessianDiagonal | map_values) {
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BOOST_FOREACH(Vector& v, state_.hessianDiagonal | map_values) {
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for (size_t aa = 0; aa < v.size(); aa++) {
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for (int aa = 0; aa < v.size(); aa++) {
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v(aa) = std::min(std::max(v(aa), min_diagonal_), max_diagonal_);
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v(aa) = std::min(std::max(v(aa), min_diagonal_), max_diagonal_);
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v(aa) = sqrt(v(aa));
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v(aa) = sqrt(v(aa));
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}
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}
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@ -141,13 +141,14 @@ GaussianFactorGraph LevenbergMarquardtOptimizer::buildDampedSystem(
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size_t dim = key_value.value.dim();
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size_t dim = key_value.value.dim();
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Matrix A = Matrix::Identity(dim, dim);
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Matrix A = Matrix::Identity(dim, dim);
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//Replace the identity matrix with diagonal of Hessian
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//Replace the identity matrix with diagonal of Hessian
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if (params_.diagonalDamping)
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if (params_.diagonalDamping){
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try {
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try {
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A.diagonal() = state_.hessianDiagonal.at(key_value.key);
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A.diagonal() = state_.hessianDiagonal.at(key_value.key);
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} catch (std::exception e) {
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} catch (std::exception e) {
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// Don't attempt any damping if no key found in diagonal
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// Don't attempt any damping if no key found in diagonal
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continue;
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continue;
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}
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}
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}
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Vector b = Vector::Zero(dim);
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Vector b = Vector::Zero(dim);
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SharedDiagonal model = noiseModel::Isotropic::Sigma(dim, sigma);
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SharedDiagonal model = noiseModel::Isotropic::Sigma(dim, sigma);
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damped += boost::make_shared<JacobianFactor>(key_value.key, A, b, model);
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damped += boost::make_shared<JacobianFactor>(key_value.key, A, b, model);
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