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Merge pull request #187 from michaelbosse/fix_LM_convergence_test 

Fix LM linearizedCostChange for robust noise models
release/4.3a0
Frank Dellaert 2020-02-29 12:25:07 -08:00 committed by GitHub
parent dd75dba801 1324cfd283
commit f02aa1bdd8
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GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 11 additions and 6 deletions
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6
gtsam/linear/JacobianFactor.cpp
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@ -513,8 +513,10 @@ Vector JacobianFactor::error_vector(const VectorValues& c) const {
/* ************************************************************************* */
double JacobianFactor::error(const VectorValues& c) const {
Vector weighted = error_vector(c);
return 0.5 * weighted.dot(weighted);
Vector e = unweighted_error(c);
// Use the noise model distance function to get the correct error if available.
if (model_) return 0.5 * model_->distance(e);
return 0.5 * e.dot(e);
}
/* ************************************************************************* */

11
gtsam/nonlinear/LevenbergMarquardtOptimizer.cpp
View File

@ -158,10 +158,13 @@ bool LevenbergMarquardtOptimizer::tryLambda(const GaussianFactorGraph& linear,
if (params_.verbosityLM >= LevenbergMarquardtParams::TRYDELTA)
delta.print("delta");
// cost change in the linearized system (old - new)
// Compute the old linearized error as it is not the same
// as the nonlinear error when robust noise models are used.
double oldLinearizedError = linear.error(VectorValues::Zero(delta));
double newlinearizedError = linear.error(delta);
double linearizedCostChange = currentState->error - newlinearizedError;
// cost change in the linearized system (old - new)
double linearizedCostChange = oldLinearizedError - newlinearizedError;
if (verbose)
cout << "newlinearizedError = " << newlinearizedError
<< " linearizedCostChange = " << linearizedCostChange << endl;
@ -188,8 +191,8 @@ bool LevenbergMarquardtOptimizer::tryLambda(const GaussianFactorGraph& linear,
// cost change in the original, nonlinear system (old - new)
costChange = currentState->error - newError;
if (linearizedCostChange >
1e-20) { // the (linear) error has to decrease to satisfy this condition
if (linearizedCostChange > std::numeric_limits<double>::epsilon() * oldLinearizedError) {
// the (linear) error has to decrease to satisfy this condition
// fidelity of linearized model VS original system between
modelFidelity = costChange / linearizedCostChange;
// if we decrease the error in the nonlinear system and modelFidelity is above threshold