31 Values linearizationPoint_;
35 std::optional<double> marginalThreshold_;
47 : marginalThreshold_(marginalThreshold) {}
83 std::optional<size_t> maxNrLeaves = {},
84 const std::optional<Ordering> givenOrdering = {});
98 Ordering getOrdering(
const HybridGaussianFactorGraph& factors,
99 const KeySet& newFactorKeys);
110 std::pair<HybridGaussianFactorGraph, HybridBayesNet> addConditionals(
111 const HybridGaussianFactorGraph& graph,
112 const HybridBayesNet& hybridBayesNet)
const;
121 HybridGaussianConditional::shared_ptr gaussianMixture(
size_t index)
const;
124 const HybridBayesNet& hybridBayesNet()
const;
135 void relinearize(
const std::optional<Ordering> givenOrdering = {});
138 Values linearizationPoint()
const;
141 HybridNonlinearFactorGraph allFactors()
const;
150 double error(
const HybridValues& x)
const;
160 double error(
const VectorValues& x)
const;
164 Ordering maybeComputeOrdering(
const HybridGaussianFactorGraph& updatedGraph,
165 const std::optional<Ordering> givenOrdering);
177 HybridGaussianFactorGraph removeFixedValues(
178 const HybridGaussianFactorGraph& graph,
179 const HybridGaussianFactorGraph& newFactors);
Point3 optimize(const NonlinearFactorGraph &graph, const Values &values, Key landmarkKey)
Optimize for triangulation.
Definition triangulation.cpp:178
A hybrid Bayes net is a collection of HybridConditionals, which can have discrete conditionals,...
Definition HybridBayesNet.h:37