gtsam::GncParams< BaseOptimizerParameters > Class Template Reference

Public Member Functions
	GncParams (const BaseOptimizerParameters &baseOptimizerParams)
	Constructor.
	GncParams ()
	Default constructor.
void	setLossType (const GncLossType type)
	Set the robust loss function to be used in GNC (chosen among the ones in GncLossType).
void	setMaxIterations (const size_t maxIter)
	Set the maximum number of iterations in GNC (changing the max nr of iters might lead to less accurate solutions and is not recommended).
void	setMuStep (const double step)
	Set the graduated non-convexity step: at each GNC iteration, mu is updated as mu <- mu * muStep.
void	setRelativeCostTol (double value)
	Set the maximum relative difference in mu values to stop iterating.
void	setWeightsTol (double value)
	Set the maximum difference between the weights and their rounding in {0,1} to stop iterating.
void	setVerbosityGNC (const Verbosity value)
	Set the verbosity level.
void	setKnownInliers (const std::vector< size_t > &knownIn)
	(Optional) Provide a vector of measurements that must be considered inliers. More...
void	setKnownOutliers (const std::vector< size_t > &knownOut)
	(Optional) Provide a vector of measurements that must be considered outliers. More...
bool	equals (const GncParams &other, double tol=1e-9) const
	Equals.
void	print (const std::string &str) const
	Print.

Public Attributes
BaseOptimizerParameters	baseOptimizerParams
	GNC parameters. More...
GncLossType	lossType = TLS
	any other specific GNC parameters: More...
size_t	maxIterations = 100
	Maximum number of iterations.
double	muStep = 1.4
	Multiplicative factor to reduce/increase the mu in gnc.
double	relativeCostTol = 1e-5
	If relative cost change is below this threshold, stop iterating.
double	weightsTol = 1e-4
	If the weights are within weightsTol from being binary, stop iterating (only for TLS)
Verbosity	verbosity = SILENT
	Verbosity level.
std::vector< size_t >	knownInliers = std::vector<size_t>()
	Slots in the factor graph corresponding to measurements that we know are inliers.
std::vector< size_t >	knownOutliers = std::vector<size_t>()
	Slots in the factor graph corresponding to measurements that we know are outliers.

Public Types
enum	Verbosity { SILENT = 0 , SUMMARY , VALUES }
	Verbosity levels.
typedef BaseOptimizerParameters::OptimizerType	OptimizerType
	For each parameter, specify the corresponding optimizer: e.g., GaussNewtonParams -> GaussNewtonOptimizer.

Member Function Documentation

setKnownInliers()

template<class BaseOptimizerParameters >

void gtsam::GncParams< BaseOptimizerParameters >::setKnownInliers ( const std::vector< size_t > &  knownIn )

inline

(Optional) Provide a vector of measurements that must be considered inliers.

The enties in the vector corresponds to the slots in the factor graph. For instance, if you have a nonlinear factor graph nfg, and you provide knownIn = {0, 2, 15}, GNC will not apply outlier rejection to nfg[0], nfg[2], and nfg[15]. This functionality is commonly used in SLAM when one may assume the odometry is outlier free, and only apply GNC to prune outliers from the loop closures.

setKnownOutliers()

template<class BaseOptimizerParameters >

void gtsam::GncParams< BaseOptimizerParameters >::setKnownOutliers ( const std::vector< size_t > &  knownOut )

inline

(Optional) Provide a vector of measurements that must be considered outliers.

The enties in the vector corresponds to the slots in the factor graph. For instance, if you have a nonlinear factor graph nfg, and you provide knownOut = {0, 2, 15}, GNC will not apply outlier rejection to nfg[0], nfg[2], and nfg[15].

Member Data Documentation

baseOptimizerParams

template<class BaseOptimizerParameters >

BaseOptimizerParameters gtsam::GncParams< BaseOptimizerParameters >::baseOptimizerParams

GNC parameters.

Optimization parameters used to solve the weighted least squares problem at each GNC iteration

lossType

template<class BaseOptimizerParameters >

GncLossType gtsam::GncParams< BaseOptimizerParameters >::lossType = TLS

any other specific GNC parameters:

Default loss

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The documentation for this class was generated from the following file:

• /Users/dellaert/git/github/gtsam/nonlinear/GncParams.h