12345qiupeng
/
gtsam
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

compare absolute value of error against k for Huber noise model

release/4.3a0
Varun Agrawal 2019-09-18 18:16:17 -04:00
parent 60f5ee7cf1
commit ba22688ca0
1 changed files with 3 additions and 3 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

6
gtsam/linear/NoiseModel.h
View File

@ -629,7 +629,7 @@ namespace gtsam {
/** /**
* The mEstimator name space contains all robust error functions. * The mEstimator name space contains all robust error functions.
* It mirrors the exposition at * It mirrors the exposition at
* http://research.microsoft.com/en-us/um/people/zhang/INRIA/Publis/Tutorial-Estim/node24.html * https://members.loria.fr/MOBerger/Enseignement/Master2/Documents/ZhangIVC-97-01.pdf
* which talks about minimizing \sum \rho(r_i), where \rho is a residual function of choice. * which talks about minimizing \sum \rho(r_i), where \rho is a residual function of choice.
* *
* To illustrate, let's consider the least-squares (L2), L1, and Huber estimators as examples: * To illustrate, let's consider the least-squares (L2), L1, and Huber estimators as examples:
@ -681,7 +681,7 @@ namespace gtsam {
/* /*
* This method is responsible for returning the weight function for a given amount of error. * This method is responsible for returning the weight function for a given amount of error.
* The weight function is related to the analytic derivative of the residual function. See * The weight function is related to the analytic derivative of the residual function. See
* http://research.microsoft.com/en-us/um/people/zhang/INRIA/Publis/Tutorial-Estim/node24.html * https://members.loria.fr/MOBerger/Enseignement/Master2/Documents/ZhangIVC-97-01.pdf
* for details. This method is required when optimizing cost functions with robust penalties * for details. This method is required when optimizing cost functions with robust penalties
* using iteratively re-weighted least squares. * using iteratively re-weighted least squares.
*/ */
@ -776,7 +776,7 @@ namespace gtsam {
Huber(double k = 1.345, const ReweightScheme reweight = Block); Huber(double k = 1.345, const ReweightScheme reweight = Block);
double weight(double error) const { double weight(double error) const {
return (error < k_) ? (1.0) : (k_ / fabs(error)); return (std::abs(error) < k_) ? (1.0) : (k_ / fabs(error));
} }
void print(const std::string &s) const; void print(const std::string &s) const;
bool equals(const Base& expected, double tol=1e-8) const; bool equals(const Base& expected, double tol=1e-8) const;