Conference News: Join us at ICRA 2022 in Philadelphia!

Join Us At ICRA 2022 in Philadelphia

Great news! We’re giving a tutorial workshop at the premier robotics conference, IEEE’s International Conference on Robotics and Automation (ICRA) in Philadelphia. 

Join us to learn why navigation robustness is critical in building effective robotics. This is a great opportunity to discuss real-world robotics problems such as the notorious “How do I handle uncertainties in my data?” question, i.e. multi-hypothesis and null-hypothesis data. We’ll also address emerging topics such as SLAM cloud computing and data persistence.

Find our Workshop Landing Page here.  See you in Philadelphia May 23rd – 27th!

More to follow! Keep up to date by following us on LinkedIn, join the discussion on Slack, or learn more about our Technology .

Coming soon: Marine surface vehicle example using radar for localization

One of our immediate aims is to demonstrate the NavAbility technology in real-world applications, and in this vein we’d like to give a preview of results from an exciting project.

This example is applicability to a variety of use-cases, from docking marine vehicles through to navigation of industrial indoor robots… And yes, self-driving too.

In the coming weeks we will release this as a complete working example. In the meantime, here is a short overview and preliminary results.

Seagrant REx at the MIT pavilion with Njord submersible hanging from center

REx and Radar Data

The MITSea Grant Remote Explorer (REx), shown above, is an autonomous marine surface vehicle used for diverse research applications . The vehicle is equipped with a variety of sensors, however the focus of this project is to use only the radar data for localization as it entered the Boston harbor. 

Dehann Fourie (CEO) constructed a GPS-denied navigation solution using the NavAbility stack, processing raw ROS radar payloads (shown below) into a complete factor graph to calculate a SLAM solution.

The example demonstrates how specialized data types can be created to capture and process radar information, which is simple to do using the Caesar.jl stack. A preliminary result of the solved trajectory is shown below.

Raw radar sweep extracted from ROS dataset
The solved trajectory (light-blue) of the vehicle as it travels into the harbor (poses highlighted).

Complete application example to follow soon

Keep an eye out for the complete example in the coming weeks!

Intrigued? Imagine what we can do with your robotics application! Please reach out to us at if you’re curious or have any questions.