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Caesar is a modern robotic framework for localization and mapping, reducing the barrier of entry for Simultaneous Localization and Mapping (SLAM). Caesar attempts to address a number of issues that arise in normal SLAM solutions - solving under-defined systems, inference with non-Gaussian measurement distributions, simplifying factor creation, and centralizing factor-graph persistence with databases. Caesar started as part of the thesis "Multi-modal and Inertial Sensor Solutions for Navigation-type Factor Graphs" [1].

Focus Area

This project focuses on the open development and progression to a public, stable, growing and usable inference library suited to data-fusion aspects of device navigation.

TL;DR Installation

If you want to skip ahead and add Caesar to your Julia packages, you can install the metadata registered package 'Caesar'.

Caesar can be installed for latest Julia 0.7/1.0 with:

julia> ] # to enable package manager
(v1.0) pkg> add Caesar

Unit tests can further be performed for the upstream packages as follows -- NOTE first time runs are slow since each new function call or package must first be precompiled.

(v1.0) pkg> test IncrementalInference
(v1.0) pkg> test RoME
(v1.0) pkg> test Caesar

(Q4 2018), Temporarily require development (master branch) version of RoMEPlotting.jl (2D) and Arena.jl (3D) as optional visualization packages:

(v1.0) pkg> add RoMEPlotting#master

# separately
(v1.0) pkg> add Arena#master

Caesar Framework

Caesar Core

Caesar is implemented in Julia (and JuliaPro) for a fast, flexible, dynamic and productive robot designer experience. This framework maintains good interoperability with other languages as listed in features below.

Caesar.jl is the "umbrella" framework that depends on and supports various dedicated purpose packages.

Why Julia: The implementation language of choice is Julia, for a wide variety of reasons such as agile development along side modern, high speed, type safe, multi-processor, JIT-dynamic, cross compiling (gcc and clang) and cross-platform technologies. Julia can be thought of as either {C+, Mex (done right), or a Fortran replacement}. The Caesar.jl project is expressly focussed on making this algorithmic code available to C/C++/C#/Python/Java/JS/ applications through a variety of interfaces described below. Please open issues or get in touch for more information about interops.

Caesar Core Packages

Critically, this package can operate in the conventional SLAM manner, using local memory (dictionaries), or alternatively distribute around a persisted FactorGraph through a graph database using CloudGraphs.jl, as discussed in literature 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 and back-end solver IncrementalInference.jl.

Details about the accompanying packages:

  • 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.


Caesar visualization (plotting of results, graphs, and data) is provided by 2D and 3D packages respectively:

  • 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;
  • Arena.jl package, which is a collection of 3D visualization tools.

Multilanguage Interops: Caesar SDKs and APIs

The Caesar framework is not limited to direct Julia use. The following Github projects provide access to features of Caesar in their language:

Contributions are welcome! If you are developing an extension we would like to help, please feel free to contact us (details below).


The Caesar framework has the following features:

  • Factor-graph representation of pose and sensor data
  • Localization using Multi-modal iSAM
    • Multi-core inference supporting Pose2, Pose3, Point2, Point3, Multi-modal (multi-hypothesis), IMU preintegration, KDE density, intensity map, partial constraints, null hypothesis, etc
  • Multi-modal and non-parametric representation of constraints
    • Gaussian distributions are but one of the many representations of measurement error
    • Simple, extensible framework for creation of new factor types
  • Multi-hypothesis representation in the factor-graph
  • Local in-memory solving on the device as well as database-driven centralized solving
  • Fixed-lag, continuous operation as well as off-line batch solving

Future Directions

Many future directions are in the works -- including fundamental research, implementation quality/performance, and system integration. Please see/open issues for specific requests or adding comments to an ongoing discussion. For example see ROS integration page here.

Next Steps

For installation steps, examples/tutorials, and concepts please refer to the following pages:

Pages = [
Depth = 3


This package is a work in progress. Please file issues here as needed to help resolve problems for everyone! We are tracking improvements and new endeavors in the Issues section of this repository.

In the future, Caesar will likely interact more closely with repos such as:

JuliaRobotics Code of Conduct

The Caesar repository is part of the JuliaRobotics organization and adheres to the JuliaRobotics code-of-conduct.


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

Consider citing our work:

  author = "Caesar.jl Contributors",
  title =  "Caesar.jl",
  year =   2018,
  url =    ""

Administration of the Caesar/RoME/IncrementalInference/Arena packages is currently conducted by Dehann Fourie who can be contacted for more details.