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| GncOptimizer (const NonlinearFactorGraph &graph, const Values &initialValues, const GncParameters ¶ms=GncParameters()) |
| | Constructor.
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| void | setInlierCostThresholds (const double inth) |
| | Set the maximum weighted residual error for an inlier (same for all factors). More...
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| void | setInlierCostThresholds (const Vector &inthVec) |
| | Set the maximum weighted residual error for an inlier (one for each factor). More...
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void | setInlierCostThresholdsAtProbability (const double alpha) |
| | Set the maximum weighted residual error threshold by specifying the probability alpha that the inlier residuals are smaller than that threshold.
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| void | setWeights (const Vector w) |
| | Set weights for each factor. More...
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const NonlinearFactorGraph & | getFactors () const |
| | Access a copy of the internal factor graph.
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const Values & | getState () const |
| | Access a copy of the internal values.
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const GncParameters & | getParams () const |
| | Access a copy of the parameters.
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const Vector & | getWeights () const |
| | Access a copy of the GNC weights.
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const Vector & | getInlierCostThresholds () const |
| | Get the inlier threshold.
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bool | equals (const GncOptimizer &other, double tol=1e-9) const |
| | Equals.
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Vector | initializeWeightsFromKnownInliersAndOutliers () const |
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Values | optimize () |
| | Compute optimal solution using graduated non-convexity.
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double | initializeMu () const |
| | Initialize the gnc parameter mu such that loss is approximately convex (remark 5 in GNC paper).
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double | updateMu (const double mu) const |
| | Update the gnc parameter mu to gradually increase nonconvexity.
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bool | checkMuConvergence (const double mu) const |
| | Check if we have reached the value of mu for which the surrogate loss matches the original loss.
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bool | checkCostConvergence (const double cost, const double prev_cost) const |
| | Check convergence of relative cost differences.
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bool | checkWeightsConvergence (const Vector &weights) const |
| | Check convergence of weights to binary values.
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bool | checkConvergence (const double mu, const Vector &weights, const double cost, const double prev_cost) const |
| | Check for convergence between consecutive GNC iterations.
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NonlinearFactorGraph | makeWeightedGraph (const Vector &weights) const |
| | Create a graph where each factor is weighted by the gnc weights.
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Vector | calculateWeights (const Values ¤tEstimate, const double mu) |
| | Calculate gnc weights.
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template<class GncParameters >
Set the maximum weighted residual error for an inlier (same for all factors).
For a factor in the form f(x) = 0.5 * || r(x) ||^2_Omega, the inlier threshold is the largest value of f(x) for the corresponding measurement to be considered an inlier. In other words, an inlier at x is such that 0.5 * || r(x) ||^2_Omega <= barcSq. Assuming an isotropic measurement covariance sigma^2 * Identity, the cost becomes: 0.5 * 1/sigma^2 || r(x) ||^2 <= barcSq. Hence || r(x) ||^2 <= 2 * barcSq * sigma^2.
template<class GncParameters >
Set the maximum weighted residual error for an inlier (one for each factor).
For a factor in the form f(x) = 0.5 * || r(x) ||^2_Omega, the inlier threshold is the largest value of f(x) for the corresponding measurement to be considered an inlier. In other words, an inlier at x is such that 0.5 * || r(x) ||^2_Omega <= barcSq.
1.9.3