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DistributedFactorGraphs.jl provides a flexible factor graph API for use in the [Caesar.jl](https://github.com/JuliaRobotics/Caesar.jl) ecosystem. The package supplies:
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* A standardized API for interacting with factor graphs
@@ -24,17 +24,22 @@ add DistributedFactorGraphs
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The in-memory implementation is the default, using LightGraphs.jl.
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It is recommended to use `IncrementalInference` to create factor graphs as they will be solvable.
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```julia
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using DistributedFactorGraphs
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using IncrementalInference
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```
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Both drivers support the same functions, so choose which you want to use when creating your initial DFG. For example:
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```julia
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# In-memory DFG
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dfg =LightDFG{NoSolverParams}()
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addVariable!(dfg, DFGVariable(:a))
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addVariable!(dfg, DFGVariable(:b))
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addFactor!(dfg, [v1, v2], DFGFactor{Int, :Symbol}(:f1)) # Rather use a RoME-type factor here (e.g. Pose2Pose2) rather than an Int, this is just for demonstrative purposes.
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# Initialize the default in-memory factor graph with default solver parameters.
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dfg =initfg()
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# add 2 ContinuousScalar variable types to the new factor graph
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