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A modern robotic toolkit for localization and mapping โ€“ reducing the barrier of entry for Simultaneous Localization and Mapping (SLAM).

Caesar

Towards non-parametric / parametric state estimation and navigation solutions [1]. Implemented in Julia (and JuliaPro) for a fast, flexible, dynamic and productive robot designer experience. This framework maintains good interoperability with other languages like C/C++ or Python, as listed in features below. Multi-modal (quasi-multi-hypothesis) navigation and mapping solutions, using various sensor data, is a corner stone of this package. Multi-sensor fusion is made possible via vertically integrated Multi-modal iSAM.

Critically, this package can operate in the conventional SLAM manner, using local dictionaries, or centralize around the FactorGraph through a graph database using CloudGraphs.jl, as discussed here[2]. A variety of plotting, 3D visualization, serialization, LCM middleware, and analysis tools come standard. Please see internal packages, Robot Motion Estimate [RoME.jl][rome-url] and back-end solver [IncrementalInference.jl][iif-url].

Comments, questions and issues welcome.

Dependency Structure

IncrementalInference.jl supplies the algebraic logic for factor graph inference with Bayes tree and depends on several packages itself. RoME.jl introduces nodes and factors that are useful to robotic navigation. RoMEPlotting.jl are a set of scripts that provide MATLAB style plotting of factor graph beliefs, mostly supporting 2D visualization with some support for projections of 3D.

Caesar.jl is the umbrella repo that depends on RoME.jl and others to support that 'passes through' the same functionality while introducing more. For example, interaction with database server systems, LCMCore.jl, (future ROS support), and more.

Arena.jl is a collection of 3D visualization tools and also depends on RoMEPlotting.jl for 2D visualizations.

In the future, Caesar.jl would likely interact more closely with repo's such as SensorFeatureTracking.jl, AprilTags.jl, and RecursiveFiltering.jl

Major features


tree = wipeBuildBayesTree!(fg, drawpdf=true)
inferOverTree!(fg, tree)
slamindb()
fg = Caesar.initfg(cloudGraph, session)
fullLocalGraphCopy(fg)
savejld(fg, file="test.jld", groundtruth=gt)
loadjld(file="test.jld")
visualizeallposes(fg) # from local dictionary
drawdbdirector()      # from database held factor graph
neoids, syms = foveateQueryToPoint(cloudGraph,["SESS21";"SESS38";"SESS45"], "robot", "user" point=[-9.0;9.0], fovrad=0.5 )
for neoid in neoids
    cloudimshow(cloudGraph, neoid=neoid)
end

examples/database/python/neo_interact_example.jl

julia -p10 -e "using Caesar; tcpStringBRTrackingServer()"

And many more, please see the examples folder.

Installation


Caesar.jl is registered with the regular Julia METADATA and can be installed as follows:

julia> Pkg.add("Caesar")

Please note that visualizations have been moved to the Arena.jl package and documentation can be found on the visualization page of this documentation.

Basic usage


The basic example has been moved to the visualization page.

Future targets


This is a work in progress package. Please file issues here as needed to help resolve problems for everyone!

Hybrid parametric and non-parametric optimization. Incrementalized update rules and properly marginalized 'forgetting' for sliding window type operation. We defined interprocess interface for multi-language front-end development.

Contributors


Authors directly involved with this package are:

D. Fourie, S. Claassens, P. Vaz Teixeira, N. Rypkema, S. Pillai, R. Mata, M. Kaess, J. Leonard

We are grateful for many, many contributions within the Julia package ecosystem โ€“ see the REQUIRE files of Caesar, Arena, RoME, RoMEPlotting, KernelDensityEstimate, IncrementalInference, NLsolve, DrakeVisualizer, Graphs, CloudGraphs and others for a far reaching list of contributions.

Cite


Consider citing our work:

@misc{caesarjl,
  author = "Dehann Fourie, John Leonard, Micheal Kaess, and contributors",
  title =  "Caesar.jl",
  year =   2017,
  url =    "https://github.com/dehann/Caesar.jl"
}

References


[1]  Fourie, D.: "Multi-modal and Inertial Sensor Solutions to Navigation-type Factor Graph",
     Ph.D. Thesis, Massachusetts Institute of Technology Electrical Engineering and Computer Science together with Woods Hole Oceanographic Institution Department for Applied Ocean Science and Engineering, September 2017.
[2]  Fourie, D., Claassens, S., Pillai, S., Mata, R., Leonard, J.: "SLAMinDB: Centralized graph
     databases for mobile robotics" IEEE International Conference on Robotics and Automation (ICRA),
     Singapore, 2017.

Manual Outline