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DistributedFactorGraphs.jl provides a flexible factor graph API for use in the Caesar.jl ecosystem. The package supplies:
Note this package is still under initial development, and will adopt parts of the functionality currently contained in IncrementalInference.jl.
Please see the documentation and the unit tests for examples on using DistributedFactorGraphs.jl.
DistributedFactorGraphs can be installed from Julia packages using:
add DistributedFactorGraphs
The in-memory implementation is the default, using Graphs.jl.
It is recommended to use IncrementalInference
to create factor graphs as they will be solvable.
using DistributedFactorGraphs
using IncrementalInference
Both drivers support the same functions, so choose which you want to use when creating your initial DFG. For example:
# In-memory DFG
# Initialize the default in-memory factor graph with default solver parameters.
dfg = initfg()
# add 2 ContinuousScalar variable types to the new factor graph
addVariable!(dfg, :a, ContinuousScalar)
addVariable!(dfg, :b, ContinuousScalar)
# add a LinearRelative factor
addFactor!(dfg, [:a, :b], LinearRelative(Normal(10.0,1.0)))