Caesar.jl
  • Welcome
  • Introduction
    • Introduction
    • Gaussian vs. Non-Gaussian
    • Installation
    • Using Julia
    • FAQ
  • Getting Started
    • Initial Concepts
    • Building Graphs
    • Solving Graphs
    • Interact w Graphs
    • Multi-Modal/Hypothesis
    • Parallel Processing
    • [DEV] Parametric Solve
  • Examples
    • Caesar Examples
    • Canonical 1D Example
    • Underconstrained Range-only
    • Hexagonal 2D SLAM
    • Fixed-Lag Solving 2D
    • Dead Reckon Tether
  • Graph Library
    • Canonical Generators
    • Variables/Factors
    • Entry=>Data Blob
    • Images and AprilTags
    • Pointclouds and PCL
    • Using Manifolds.jl
    • Flux (NN) Factors
  • Visualization
    • Installing Viz
    • Plotting (2D)
    • Visualization (3D)
  • Middlewares
    • ROS Middleware
    • Compile Binaries
    • Zero Install Solution
    • Multi-session/agent Solving
    • Multi-Language Support
  • How to Expand?
    • Pkg Framework
    • Custom Variables
    • Custom Prior Factor
    • Custom Relative Factor
    • Caching and Stashing
    • Important Factor Features
    • Variable/Factor Considerations
    • More Functions
  • Principles
    • Filters vs. Graphs
    • Generic Convolutions
    • Multiplying Functions (.py)
    • Bayes (Junction) tree
    • Advanced Bayes Tree Topics
    • Non-Gaussian Algorithm
  • Developer Zone
    • Wiki Pointers
    • Legacy Factors
    • Creating DynPose Factor
    • Known Issue List
    • Internal Functions
  • Literature
    • References
Version
  • Graph Library
  • Flux (NN) Factors
  • Flux (NN) Factors
GitHub

Incorporating Neural Network Factors

IncrementalInference.jl and RoME.jl has native support for using Neural Networks (via Flux.jl) as non-Gaussian factors. Documentation is forthcoming, but meanwhile see the following generic Flux.jl factor structure. Note also that a standard Mixture approach already exists too.

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