new HybridSmoother::relinearize method
parent
82fc21a7e6
commit
d27583ba92
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@ -101,6 +101,12 @@ void HybridSmoother::update(const HybridNonlinearFactorGraph &newFactors,
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std::optional<size_t> maxNrLeaves,
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const std::optional<Ordering> given_ordering) {
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HybridGaussianFactorGraph linearizedFactors = *newFactors.linearize(initial);
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// Record the new nonlinear factors and
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// linearization point for relinearization
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allFactors_.push_back(newFactors);
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linearizationPoint_.insert_or_assign(initial);
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const KeySet originalNewFactorKeys = newFactors.keys();
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#ifdef DEBUG_SMOOTHER
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std::cout << "hybridBayesNet_ size before: " << hybridBayesNet_.size()
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@ -28,6 +28,7 @@ namespace gtsam {
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class GTSAM_EXPORT HybridSmoother {
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private:
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HybridNonlinearFactorGraph allFactors_;
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Values linearizationPoint_;
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HybridBayesNet hybridBayesNet_;
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/// The threshold above which we make a decision about a mode.
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@ -125,6 +126,16 @@ class GTSAM_EXPORT HybridSmoother {
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/// Optimize the hybrid Bayes Net, taking into accound fixed values.
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HybridValues optimize() const;
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void relinearize() {
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allFactors_ = allFactors_.restrict(fixedValues_);
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HybridGaussianFactorGraph::shared_ptr linearized =
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allFactors_.linearize(linearizationPoint_);
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HybridBayesNet::shared_ptr bayesNet = linearized->eliminateSequential();
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HybridValues delta = bayesNet->optimize();
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linearizationPoint_ = linearizationPoint_.retract(delta.continuous());
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reInitialize(*bayesNet);
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}
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private:
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/// Helper to compute the ordering if ordering is not given.
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Ordering maybeComputeOrdering(const HybridGaussianFactorGraph& updatedGraph,
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@ -287,6 +287,8 @@ class HybridSmoother {
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std::optional<size_t> maxNrLeaves = std::nullopt,
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const std::optional<gtsam::Ordering> given_ordering = std::nullopt);
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void relinearize();
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gtsam::Ordering getOrdering(const gtsam::HybridGaussianFactorGraph& factors,
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const gtsam::KeySet& newFactorKeys);
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