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IncrementalInference.jl

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Optimization routines for incremental non-parametric and parametric solutions based on factor graphs and the Bayes (Junction) tree implemented in the Julia language (and JuliaPro).

Introduction

This package implements Multi-Modal iSAM (MM-iSAM) algorithm (see references) which does hybrid non-parametric and parametric inference/state-estimation over large factor graphs. Inference is performed via the Bayes (junction) tree where non-parametric and parametric solutions are based on marginal-joint belief propagation (an approximate sum-product algorithm). Many benefits such as clique recycling is available. See the common Caesar.jl documenation for more details. Note, that IncrementalInference.jl does not have to be used with Caesar.jl, and aims to only implement the inference operations against mathematical abstractions such as Manifolds.jl.

This package forms a cardinal piece of the Caesar.jl robotics toolkit, including 3D visualization and database interaction, which can serve as a base station for a robotic platform. A standalone Robot Motion Estimate package extends the available variables, factors, and utilities for use in robotic navigation.

Please contact info@navability.io for further support on this package, NavAbility.

Installation

Install the package from inside Julia

(v1.6) pkg> add IncrementalInference

Pre-install the following packages system wide packages[, and easily draw factor graph and Bayes tree]:

sudo apt-get install hdf5-tools
sudo apt-get install graphviz xdot # optional

Examples

This library is built as a back-end solver which is closer to the mathetical operations can be easily modified and extended for a variety of uses. Specific emphasis is placed on allowing outside user defined variables and factor definitions to be used. The current major use case is through RoME.jl and Caesar.jl which introduces various sensor measurement and motion manifold functions for use in Robot Motion Estimate (a.k.a SLAM). See these and related packages for documentation and examples.

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Cite and Contributors

We are grateful for many, many contributions within the Julia package ecosystem -- see the Project.toml files for a far reaching list of upstream packages and contributions.

Consider citing our work using the common reference at Caesar.jl Citation with IncrementalInference.jl DOI

Get Involved, and Code of Conduct

This project adheres to the JuliaRobotics code of conduct, and we invite contributions or comments from the community. Use the slack channel, Julia Discourse, or Github issues to get in touch.

References

See references of interest here