ICRA 2022 NavAbility Tutorial Workshop on Non-Gaussian SLAM and Computation

Welcome to the ICRA 2022 Non-Gaussian SLAM tutorial. This hands-on workshop will take you through the some of the canonical issues motivating Non-Gaussian SLAM, and demonstrate how real-world problems (like false loop-closures and distributing computations) are addressed with this emerging technique. 

Furthermore, the DIY examples in this tutorial will demonstrate how the same modeling philosophy is readily extendable to other non-Gaussian behavior, and how this new modeling freedom can simplify SLAM front-end processes.

Learn more about the four drivers of non-Gaussian behavior in SLAM.

The examples are packaged for a zero-install, “bring-your-own-laptop” setup so that you can easily experiment with non-Gaussian SLAM code, and the results are published if you would prefer to simply peruse through the examples.


Note: The tutorials will be provided both in-person as well as remotely via Gather.Town to allow for both physical and remote participation. Please feel free to join us from anywhere in the world via Gather.Town!

What you will learn in this workshop

Five tutorials will be provided to demonstrate new avenues in Non-Gaussian SLAM. These short tutorials will:

    • Demonstrate pros and cons of Non-Gaussian (multi-modal) SLAM processing
    • Show how to pose challenging robotics localization and mapping problems as a non-Gaussian factor graph with SLAM variables and factors
    • Illustrate how to incorporate imperfect information that is often found in real-world data
    • Demonstrate how the factor graphs can be solved locally and in the cloud to derive an estimate of the path traveled by the vehicle
    • Provide an overview of visualization tools to analyze and examine regions of interest
    • Discuss how to extend the examples to address your SLAM robotics problem

Who Should Attend


Researchers in the fields of SLAM and robotics looking to go beyond the unimodal assumption

Industry Roboticists

Industry leaders looking to add robustness, trust, and safety to their applications

New Robotics Engineers

New roboticists learning about SLAM algorithms, data persistence, and SLAM in the cloud


The following DIY canonical examples (incorporating real-world use-cases) are provided to guide the reader on the four main drivers of non-Gaussian behavior in SLAM. 

You can work through any, or all, of the following examples during the day:

    1. Creating and Solving Factor Graphs: An introduction to building and solving factor graphs
    2. Non-Gaussian Measurements and Estimation: A conceptual overview of 1D Ambiguous Measurements and how to incorporate them in factor graphs
    3. Solving the Wifi Ranging Localization Problem: An under-determined Range-only SLAM problem that is pervasive, like determining position from Wifi signal strength
    4. The Power of Contradictory, Dynamic, and Prior Data: Solving contradictory data problems using multi-hypothesis factors
    5. End-to-End Marine Vehicle Application: A real-world marine non-Gaussian SLAM solution with radar data


The tutorials are designed to be:

    • Zero-footprint setups that should take about 15 minutes to take you from problem definition to results and analysis
    • Run in a browser in a JupyterHub Notebook independently or pull the code to your local machine to review later
    • Partially guided in open an open forum setting – all tutorials will always be available and can be run at any time, however we will guide attendees through the problem, the results, and the relevance of the outcomes during each session
    • Interactive with time and resources set aside for discussions, so please feel free raise any questions at any point during the tutorials

More Information

For more information please feel free to reach out to us at any point for general information at info@navability.io.

For specific questions regarding the tutorials please feel free to reach out to us directly:

Dehann Fourie


Sam Claassens


Jim Hill

Technical Operations

Johan Terblanche

Remote Operations

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